If AI isn’t the main reason for these layoffs, you could be preparing for the wrong threat. The better question isn’t, “Is AI taking our jobs?” but rather, “Why is it so useful for companies to say that it is?” This newsletter explores the real story behind the headlines—a story that’s less about technology and more about Wall Street, and what it means for your career strategy.

Watch the full episode here:

Timestamp:

00:00 What If We're Wrong About AI Layoffs?
01:06 The Story We Keep Hearing
01:58 Why This Matters for Your Career
02:46 The Right Question to Ask
03:37 The Stock Market Reward
04:52 Why It's So Hard to Prove
06:00 The Quiet Rehiring
07:14 The Real Damage Isn't the Layoff
08:15 Your New Career Strategy

More on YouTube related to this episode:

Forward Deployed Engineer is The Hottest AI Job

The Best Careers In The Age Of AI

The Biggest Mistake Developers Are Making With AI (Ft. GitHub Executives)

Should You Become an AI Engineer?

Should You Still Become a Software Engineer in 2026? GitHub VP

Key Lessons

Read more

It turns out that the same skills used to manage engineering teams are now the skills you need to manage AI agents. In this guide, we’ll break down the essential features and mental models you need to manage an entire project with AI, from idea to deployment, even if you’re a complete beginner.

Watch the full episode here:

Timestamp:

00:00 Most People Use AI Wrong
00:38 What is Codex and Why use it?
01:25 Getting Set Up
02:18 Finding Your Way Around
03:30 The Plugins You Actually Need
05:02 Our First Project: Email Summary
06:18 Give Codex Access to Your Tools
06:43 Using Special Commands
07:21 Code Review
07:45 Skills

Key Lessons

Read more

As a former engineering manager at Meta who has hired hundreds of engineers, I want to show you what’s actually behind those headline numbers. If you plan your career based on a headline, you’re likely to be disappointed. In this newsletter, I’ll walk you through the hiring manager’s playbook on AI engineer pay, based on real, recent data.

Watch the full episode here:

Timestamp:

0:00 AI Engineer Salary

0:36 The Number Everyone Sees

1:26 — Go To The Source

2:30 — Junior AI Engineers

4:26 — Resources

5:54 — Mid-Level AI Engineers

7:53 — Senior Engineer

9:47 — Remote AI Engineer

More on YouTube related to this episode:

• Forward Deployed Engineer is The Hottest AI Job

• The Best Careers In The Age Of AI

• The Biggest Mistake Developers Are Making With AI (Ft. GitHub Executives)

• Should You Become an AI Engineer?

• Should You Still Become a Software Engineer in 2026? GitHub VP

Key Lessons

Read more

Every week I see software engineers asking the same question:

“What skills should I actually be learning?”

Most answers are opinions.

I wanted data.

So I analyzed 724 qualified job postings across 16 technology roles to see how hiring requirements have changed from 2024 to 2026.

Instead of looking at hype, I looked at what employers are actually asking for.

Some of the findings surprised me.

• AI Engineers are now expected to know AI Agents, RAG, AWS, and cloud infrastructure, not just Python and model development.

• CI/CD continues to spread into more software engineering roles.

• Several technologies that were common just a couple of years ago have largely disappeared from the top requested skills.

• Different roles are diverging much faster than many people realize. The skills that help a Data Scientist are increasingly different from those expected of an AI Engineer or Machine Learning Engineer.

The report covers:

• AI Engineer

• Machine Learning Engineer

• Data Scientist

• Data Engineer

• Software Developer

• Front-End Engineer

• Back-End Engineer

• Full-Stack Engineer

• DevOps Engineer

• MLOps Engineer

• Security Engineer

• Prompt Engineer

• Product Manager

• Business Analyst

• Data Analyst

• Forward Deployed Engineer

For each role you’ll find:

• Top 10 employer-requested skills

• Rankings compared with previous years (where available)

• New and emerging technologies

• Skills that are declining

• A visual breakdown of core, emerging, niche, and declining skills

• A concise interpretation of what the changes mean

Why is it important to include the right keywords on your resume?

Recruiters are overwhelmed by the volume of resumes they receive and do not have time to review every one in depth.

If your resume is filled with random keywords or irrelevant information, it can quickly land in the “No” pile.

Think of it like a tourist restaurant serving sushi, hot pot, burgers, and pizza—does that sound like a quality restaurant?

Customize your resume for the job. Ditch the random skills and focus on your strongest projects that are relevant to

the role you’re pursuing. Be the Jiro—the three-Michelin-starred sushi restaurant—of resumes.

A tailored resume grabs attention. Use the same keywords employers use in their job descriptions and stand out from

the crowd. This report helps you identify which skills employers currently emphasize for each technology role—and

how those requirements have shifted.

— Adapted from page 9 of The Ultimate Resume Handbook.

Keyword Resources to Get You Started

I compiled the top keywords for 16 of the hottest jobs in tech. We analyzed 724 qualified job postings and identified

the ten most frequently used keywords for each role.

Python appeared in 84.5% of Data Scientist, 79.7% of Machine Learning Engineer, and 74.3% of AI Engineer postings,

remaining a strong skill in 2026, as it was in 2024. Focus on keywords relevant to the role and supported by your

experience. Then demonstrate those skills through meaningful projects and accomplishments.

Let’s dive into the keywords!

Download the report

The full 39-page PDF is available below.

Read more

This role looks suspiciously familiar to jobs that have existed for decades.

Let’s break down what a Forward Deployed Engineer actually does, why this rebrand is happening now, and how to decide if it’s the right move for your career.

Watch the full episode here:

Timestamp:

0:00 Forward Deployed Engineering
0:00 The hottest job in AI
1:11 What companies say an FDE does
2:03 Remember the "Prompt Engineer"?
3:13 The lesson from "Online Searchers"
4:05 The job that's been here all along
5:16 Why the rebrand is happening now
6:46 Focus on the skill, not the title
7:28 Know what game you're playing
7:57 SEO Keyword List

More on YouTube related to this episode:

Should You Still Become a Software Engineer in 2026? GitHub VP
Don’t choose the Wrong AI Career in 2026 [Tier List]
The Highest Paying Jobs In The Age of AI
WTF Is Happening to Remote Jobs in Tech?
A practical Guide To Becoming An AI Engineer

Connect with me:

💻 LinkedIn: https://www.linkedin.com/in/jeanklee/

🌸 Instagram: https://www.instagram.com/jeanexplains/

Key Lessons

Read more

I dug through the research on the future of jobs to figure out what’s actually happening. In this newsletter, we’ll explore why some careers are benefiting from AI while others are being replaced, what the winning jobs are, and what you can do right now to be on the winning side.

Watch the full episode here:

Key Lessons

Read more

Key Lessons

1/ AI is not replacing engineers. It’s changing what engineers do.

The value of engineers is shifting from writing code to making decisions, defining requirements, and exercising judgment.

2/ AI is best at implementation, not decision-making.

AI can generate code quickly, but developers still need to decide what should be built, how it should work, and whether the output is actually good.

3/ Planning is becoming more important than coding.

As AI handles more implementation, engineers are spending more time creating specifications, researching options, and defining problems clearly.

4/ Human judgment remains a competitive advantage.

The engineers who get the most value from AI are not blindly accepting outputs. They’re applying taste, context, and critical thinking.

5/ Strong opinions make AI more useful.

AI performs best when given clear direction and constraints. Engineers who know what they want can use AI far more effectively than those who don’t.

6/ Creativity matters more than ever.

When implementation becomes easier, the differentiator shifts to ideas, imagination, and identifying problems worth solving.

7/ The best engineers know when not to use AI.

Future success isn’t about delegating everything. It’s about understanding when to rely on AI and when to think through problems yourself.

8/ Future-proofing means learning continuously.

The engineers who thrive aren’t waiting for stability. They’re experimenting, staying curious, and adapting as new tools emerge.

9/ More software, not fewer engineers.

Historically, every time software development becomes easier, more people build software. AI is likely to expand who can create software rather than eliminate the need for developers.

Timestamp

0:00 Trailer

1:24 What developer advocacy?

3:55 Is AI making software engineers less valuable?

6:38 What GitHub has learned from millions of Copilot users

7:38 Which tasks developers should delegate to AI?

12:29 How engineers can future-proof their careers

14:48 What separates engineers thriving with AI?

16:56 How AI compares to cloud, mobile, and previous tech shifts?

18:31 Rapid fire

20:55 Will AI replace Software Engineers?

About Martin Woodward

Martin Woodward is Vice President of Developer Relations at GitHub, where he leads the teams responsible for developer advocacy, community programs, and engagement with millions of developers worldwide. Before joining GitHub, Martin spent more than 13 years at Microsoft, where he played a key role in the company’s transition toward open source, helped introduce Git across Microsoft, contributed to the creation of the .NET Foundation, and was involved in the GitHub acquisition. With nearly three decades in software engineering, developer tools, and open source, Martin brings a unique perspective on how AI, open ecosystems, and developer communities are reshaping the future of software development.

About Cassidy Williams

Cassidy Williams is Senior Director of Developer Advocacy at GitHub, where she helps developers build better software and engage with the open source community. Over the course of her career, Cassidy has been a software engineer, startup CTO, developer advocate, educator, advisor, investor, and podcast host. She has held roles at companies including GitHub, Netlify, Amazon, CodePen, Clarifai, and Venmo, and is widely recognized as one of the most influential voices in developer education and developer experience. Through her technical content, speaking engagements, and community work, Cassidy helps developers navigate emerging technologies, AI-assisted development, and the changing landscape of software engineering.

More on YouTube related to this episode:

Should You Still Become a Software Engineer in 2026? GitHub VP

Should You Become an AI Engineer?

Don’t choose the Wrong AI Career in 2026 [Tier List]

The Highest Paying Jobs In The Age of AI

WTF Is Happening to Remote Jobs in Tech?

About Jean

I’m Jean, an ex-WhatsApp early engineer and Meta manager with over 20 years in tech. I was an early engineer at WhatsApp before its $19B acquisition by Meta, then led engineering teams at Meta, where I experienced firsthand how technology evolves and shapes the world.
Now, I’m on a mission to break down how AI is changing our careers, industries, and the future of work. AI will transform how we work, learn, and live, and many are overwhelmed by what this means, especially those without access or context.
No hype. No jargon. Just real-world insights for navigating AI and what’s next.

Connect with me

💻 LinkedIn: https://www.linkedin.com/in/jeanklee/
🌸 Instagram: https://www.instagram.com/jeanexplains/
I give advice for navigating your engineering career journey successfully.

Subscribe now

Before you go all-in, it’s important to get an honest look at what becoming an AI engineer actually involves. This newsletter breaks down the common misconceptions and the five real challenges of pursuing a career in AI, so you can make a decision based on what’s actually true.

Key Lessons

AI engineering is not what you think it is

AI engineers don’t build foundation models from scratch; they build products on top of existing ones like GPT-4 or Llama. The role is much closer to software engineering than academic research, focusing on solving business problems with AI tools.

You don’t need to be a math genius

Unlike traditional machine learning, most AI engineering roles are not math-intensive and focus more on coding, APIs, and system design. However, if you’re pursuing ML engineering specifically, a strong foundation in linear algebra, statistics, and calculus is essential.

High salaries reflect high difficulty

It’s okay to be motivated by the high salaries in AI, but understand they exist because the work is incredibly challenging. An AI engineering career is not a shortcut to a big paycheck; it’s compensation for a rare and difficult skill set.

The work is genuinely hard

Don’t underestimate the difficulty of breaking into and succeeding in AI. The right question isn’t “Is it easy?” but “Am I willing to learn hard things and persevere?” because the path requires mastering complex systems and competing against other highly qualified people.

The competition is intense

The number of people who want AI jobs is growing much faster than the number of available roles, especially for entry-level positions. Like any gold rush, this creates intense competition where only those with proven skills stand out.

Burnout is a real risk

High-paying AI jobs often come with high pressure, fast-paced environments, and a demanding culture that prioritizes speed over balance. The race to ship products before competitors means that work-life balance can be a significant trade-off.

You can’t always have both money and balance

If a sustainable, balanced lifestyle is your top priority, be aware that many of the highest-paying AI roles will conflict with that goal. You often have to choose between optimizing for compensation or optimizing for personal time.

The job is continuous re-learning

Success in AI requires you to constantly learn and adapt as new models, tools, and techniques emerge every few months. If you thrive on stability and clear processes, the constant change in this field can be exhausting.

Consider an alternative entry point

Instead of aiming for a junior AI role directly, consider starting in a related field like software or data engineering. Building a strong foundation there can make it much easier to transition into a more specialized AI role later in your career.

Also watch, Don't choose the Wrong AI Career in 2026 [Tier List] for alternative job ideas.

Watch the full episode here:

👉 Should You Become an AI Engineer?

Timestamp:

0:00 Should You Become an AI Engineer?
0:54 What Is AI Engineering?
2:28 The Math Thing and the Money Thing
3:36 The 5 Real Challenges
3:47 #1 AI Is Hard
5:48 #2 The Competition Is Intense
7:00 #3 Burnout is a Real Risk
8:17 #4 Everything Changes Constantly
9:32 #5 There Aren’t Many Junior Roles
10:05 Why Companies Prefer Experience

More on YouTube related to this episode:

Should You Still Become a Software Engineer in 2026? GitHub VP
Don’t choose the Wrong AI Career in 2026 [Tier List]
The Highest Paying Jobs In The Age of AI
WTF Is Happening to Remote Jobs in Tech?

Connect with me:

💻 LinkedIn: https://www.linkedin.com/in/jeanklee/

🌸 Instagram: https://www.instagram.com/jeanexplains/

We discussed why AI is not the end of the developer career, what junior software engineers should learn now, and how developers can build real projects that stand out in a changing tech career landscape.

Summary of Lessons

1. Every generation panics about new tools

Scott’s view is that AI anxiety is not new. Developers panicked about color syntax highlighting, autocomplete, Stack Overflow, and now AI. His point: the tool changes, but the craft of software engineering remains.

2. AI is a powerful tool, not a replacement for judgment

AI can generate code, but it does not automatically create good architecture. Developers still need to understand tradeoffs, design systems, review code, and take responsibility for what ships.

3. Treat AI-generated code like code from a stranger

Scott compares AI output to a pull request from an anonymous person on the internet. You should not blindly trust it. Use tests, CI/CD, code review, dependency checks, and a proper software development lifecycle.

4. Human responsibility matters more, not less

If AI creates a bad feature and you merge it, that is still your responsibility. The engineer owns the product, the user experience, and the consequences of the code.

5. AI can create unhealthy productivity dopamine

Scott warns that developers can start “vibrating” with AI enthusiasm. The lesson is to slow down, think clearly, and ask whether you are solving the right problem in a healthy way.

6. Junior engineers still matter

One of his strongest points: the software engineering profession will collapse if companies stop hiring early-career developers. Today’s juniors become tomorrow’s seniors.

7. Senior engineers should be judged by how many seniors they create

Scott argues that mentorship should be formalized. A senior engineer’s role is not just to ship code, but to create more capable engineers.

8. Learn the fundamentals

If he were starting over, Scott would focus on basics: HTTP, DNS, distributed systems, deadlocks, and core software engineering concepts. AI can help you learn faster, but it cannot replace understanding.

9. Communication is part of programming

Programming is not just writing syntax. It is expressing intent clearly so humans, compilers, tests, and AI tools can understand what you mean.

10. Make projects that actually work

Do not just build another tic-tac-toe clone or one-shot a Minecraft clone. Build something useful for your life, family, health, community, church, mosque, hobbies, or interests. Real projects show agency.

A project is more impressive when it is maintained, tested, deployed, documented, and useful.

11. Developers often overcomplicate systems

Scott emphasized simple architecture: static sites, JSON files, caching, GitHub Issues as a database, and avoiding unnecessary infrastructure. The lesson: use the simplest system that solves the problem.

12. Software engineering is still worth learning

His answer to “Will AI replace software engineers?” was: “It better not.” The broader lesson is that the future developer role changes, but the need for people who can understand problems and build reliable systems remains.

Timestamp

0:00 Trailer
1:14 How This Transition Compares To Previous Ones?
2:32 What Percentage Would You Say is AI-assisted In Your Workflow?
4:55 Is Thinking Still On Us?
5:27 What Projects Are You Working On?
7:35 Best Practices for AI Coding?
9:27 How Should Beginners Learn with AI?
12:57 What would you do if you wanted to land a job in 12 months?
14:34 Do Personal Projects Help Land Jobs?
16:12 Do You Have More Demos to Show As Inspiration for People?
20:50 How Do You Manage Your Time?
23:00 Rapid Fire

About Scott

Scott Hanselman is Vice President and Member of Technical Staff at Microsoft, where he works on AI-powered developer tools across Microsoft, GitHub, and Windows. A programmer, architect, teacher, author, and longtime developer advocate, Scott has spent decades helping developers build software and adapt to major technology shifts. In this conversation, we discuss AI coding agents, the future of software engineering, and why he believes there may be more opportunities than ever for the next generation of developers.

More on YouTube related to this episode:

• A practical Guide To Becoming An AI Engineer

• WTF Is Happening to Remote Tech Jobs?

• I Asked 5 Tech Leaders What the Tech Job Market Really Wants Now

• Should You Still Study Computer Science in the Age of AI?

• What AI Actually Means for Software Engineers

Connect with me

💻 LinkedIn: https://www.linkedin.com/in/jeanklee/

🌸 Instagram: https://www.instagram.com/jeanexplains/

Subscribe now

Key Lessons

1. AI Engineer is not the only path into AI

Many people assume they need to become an AI Engineer to benefit from AI, but there are valuable opportunities throughout the broader AI ecosystem.

2. ML Engineering is becoming increasingly competitive

Many ML Engineer positions attract candidates with advanced degrees, research experience, and strong math backgrounds, making the field harder to enter than most people realize.

3. The biggest opportunities may be around AI, not inside model building

Infrastructure, deployment, operations, sales, and implementation are all growing alongside AI adoption.

4. Some jobs remain highly human-centered

Understanding human behavior, emotions, and decision-making is still difficult for AI.

5. Jobs involving the physical world have strong long-term potential

Roles like Controls Engineer and Data Center Design Engineer are tied to robotics, infrastructure, and real-world systems that cannot be fully automated away.

6. Some careers can be a useful stepping stone

Many AI adjacent tech jobs can still serve as a pathway into product, operations, or other technology roles.

7. Careers are rarely linear

Many successful professionals move across different functions throughout their careers. The ability to adapt and pivot may matter more than choosing the perfect role on day one.

More on YouTube related to this episode:

A practical Guide To Becoming An AI Engineer

The Highest Paying Jobs In The Age of AI

WTF Is Happening to Remote Tech Jobs?

I Asked 5 Tech Leaders What the Tech Job Market Really Wants Now

Should You Still Study Computer Science in the Age of AI?

Connect with me

💻 LinkedIn: https://www.linkedin.com/in/jeanklee/

✉️ Substack: https://substack.com/@exaltitude

🌸 Instagram: https://www.instagram.com/jeanexplains/

I give advice for navigating your engineering career journey successfully.

I sat down with Sanjar Atamuradov, founder of Humanola, to discuss why robotics is reaching an inflection point, which jobs are most vulnerable to automation, and what skills engineers need to stay relevant as AI moves beyond software and into physical work.

Key Takeaways

1. Robotics Is Going Through a Major Shift

For decades, robots relied on hard-coded rules and control systems. Today, advances in AI and neural networks are making robots far more adaptable, allowing them to learn tasks and operate in environments that were previously impossible.

2. The Biggest Bottleneck Is Data

Training robots requires enormous amounts of real-world interaction data. Unlike language models that learn from internet text, robots need physical experience to understand how objects move, interact, and respond to forces.

3. Teleoperation Is Teaching Robots How Humans Work

One of the primary ways robotics companies collect data today is through teleoperation, where humans remotely control robot arms and humanoid robots. Every movement becomes training data that helps robots learn physical tasks.

4. Robots Can’t Simply Learn From YouTube

While many people assume robots can learn from videos online, current robotics systems require much richer data, including camera views, hand movements, joint angles, and sensor information. Physical intelligence is significantly harder than digital intelligence.

5. Humanoid Robots Will Arrive First In Structured Environments

Warehouses, logistics centers, and repetitive industrial workflows are likely to see widespread humanoid robot adoption before homes. Structured environments are far easier for robots to navigate than unpredictable human spaces.

6. Some Jobs Are Much Harder To Automate Than People Think

Jobs involving highly variable environments and physical dexterity, such as plumbers and HVAC technicians, remain extremely challenging for robots. Repetitive tasks in warehouses and assembly lines are much easier targets for automation.

7. AI Still Doesn’t Replace System-Level Thinking

AI coding tools can accelerate implementation, but engineers are still responsible for architecture, debugging, problem solving, and understanding complex systems. The ability to reason about systems remains a key differentiator.

8. Robotics Is One Of The Best Engineering Fields To Enter

As physical AI advances, demand is growing for robotics engineers, software engineers, machine learning engineers, electrical engineers, and specialists who can build and train intelligent robotic systems.

Timestamp

=====

0:00 Trailer

1:31 Testing the robot’s balance

1:53 How Sanjar got into robotics

2:33 Why humanoid robots are changing now

4:02 When humanoid robots enter daily life

5:01 Why home robots are harder

6:12 What Humanola does

7:13 Why robotics data matters

7:59 What teleoperation means

10:22 Can robots learn from YouTube?

11:24 What skills matter if AI writes code?

13:10 Are robotics engineers safer from AI?

13:49 Jobs robots may replace first

14:52 Jobs robots may create

15:44 Career paths in robotics

16:54 What young engineers misunderstand

About Sanjar

======

Sanjar Atamuradov is a robotics entrepreneur and researcher focused on advancing physical intelligence through general-purpose robotics. He is the Founder and CEO of Humanola, a Bay Area startup dedicated to accelerating the development of intelligent robotic systems. Previously, Sanjar conducted humanoid locomotion and manipulation research at the Georgia Institute of Technology and earned a degree in Computer Science from KAIST. His experience spans humanoid and quadruped robotics, autonomous navigation, computer vision, machine learning, control systems, and robotics software, with hands-on expertise developing perception, planning, and locomotion systems across both academic labs and industry startups.

🎙️More on YouTube related to this episode:

========================

The Robotics Gap: Hype vs Reality

WTF Is Happening to Remote Tech Jobs?

A practical Guide To Becoming An AI Engineer

What Matters More Than a Computer Science Degree Today

I Asked 5 Tech Leaders What the Tech Job Market Really Wants Now

Should You Still Study Computer Science in the Age of AI?

What AI Actually Means for Software Engineers

📣✨Connect with me

========================

💻 LinkedIn: https://www.linkedin.com/in/jeanklee/

✉️ Newsletter: https://exaltitude.substack.com/

🌸 Instagram: https://www.instagram.com/jeanexplains/

I give advice for navigating your engineering career journey successfully.

This roadmap is not a quick path. Programs like the Stanford AI Graduate Certificate typically take 1 to 2 years part-time. If you’re self-studying, your timeline will depend on a few things:

• Your starting point: If you already have experience with programming or math, you’ll move faster.

• Your time commitment: A few hours a week will look very different from daily focused work.

• Your depth of learning: You can rush through concepts, or you can actually understand them well enough to build with them.

There’s no single timeline. What matters is not how fast you finish, but whether you can actually use what you learn. This roadmap is designed to get you to that point.

Watch the video guide

Phase 1: Programming Fundamentals

Python

• Goals: Become proficient in Python syntax and libraries.

• Free Classes/Resources:

  • Python Roadmap by Jean

• Recommended Book: Automate the Boring Stuff

• Estimated Time: 4-6 weeks

Python Libraries

• Goals: Familiarize yourself with popular libraries.

• Classes/Resources:

  • Numpy Tutorial by Jean

  • Pandas Tutorial

• Estimated Time: 2-4 weeks

SQL

• Goals: Learn how to query, filter, and analyze data using SQL. Understand how to work with relational databases, including selecting data, joining tables, aggregating results, and writing efficient queries for real-world use cases.

• Classes/Resources:

  • Introduction to SQL by DataCamp (paid)

  • SQL Cheatsheet by Jean

  • W3 School SQL Tutorial

• Estimated Time: 2–4 weeks

Linux Command Line

• Goals: Master essential Linux commands and shell scripting.

• Classes/Resources:

  • Linux Academy

  • Introduction to Shell by DataCamp (paid)

• Recommended Written Tutorial: Ubuntu’s The Linux command line for beginners

• Estimated Time: 1-2 weeks

Phase 2: AI Foundations

All-in-one courses to build an AI Foundation

• AI For Everyone

• Google AI Professional Certificate

• Microsoft AI & ML Engineering

Special offer:

Get Coursera Plus for 40% OFF 🎉
CLICK HERE

Understanding Foundation Models

• Goals: Understand what foundation models are and how they work at a high level. Learn how training, post-training, and scaling impact model behavior so you can choose the right model for different use cases.

• Key Topics:

  • Training data

  • Modeling

  • Post-training

• Class:

  • Foundation Models Tutorial, and Why Not to Fine Tune Them by Stanford

  • MIT 6.S191 (2025): Large Language Models (Google) by MIT

• Written Resources:

  • AI Engineering: Building Applications with Foundation Models by Chip Huyen (paid book)

  • [Gopher] Scaling Language Models: Methods, Analysis & Insights from Training Gopher (DeepMind, 2021): Introduced the modern large-scale scaling paradigm and analyzed how performance improves as models grow.

  • [InstructGPT] Training language models to follow instructions with human feedback (OpenAI, 2022): Showed how human feedback and preference optimization transformed raw LLMs into helpful assistants.

Sampling

• Goals: Understand how models generate text and how sampling controls output style, including randomness, creativity, and determinism. Learn how parameters like temperature and top-p affect model behavior in real applications.

• Resource:

  • Optimize Your AI Models by Matt Williams.

  • Sampling for Text Generation by Chip Huyen: A clear explanation of how decoding works in practice, covering temperature, nucleus sampling, beam search, test-time compute, and why sampling changes output style.

Read more

Summary of Lessons

Unconventional Career Path
→ Your unique background can become your biggest advantage. Mindy built her career by applying psychology and human behavior research to technology, not by following a traditional technical path.

A Powerful Moment Working in AI
→ The most impressive AI breakthroughs often come from small everyday problems. Features like automatically cleaning up speech-to-text and translating across languages revealed how transformative AI can be in practice.

Learning In The Age of AI
→ The best AI learning tools don’t just give answers. They help people understand concepts, build mental models, and develop real problem-solving skills.

Will AI Replace Us?
→ AI should eliminate tedious work, not meaningful work. The goal is to automate routine tasks so people can spend more time on relationships, creativity, and what matters most to them.

Career Advice from Google VP
→ Technical skills matter, but strategic problem-solving and understanding user needs matter more. The ability to learn quickly and think across disciplines is becoming a major career advantage.

About Mindy Brooks

Mindy Brooks is the Vice President of Product Management at Google, where she leads the Android platform and shapes how AI-powered experiences reach billions of users worldwide. With a background in developmental and educational psychology from New York University, she began her career studying how people learn and behave, including leading research at Sesame Workshop and later consumer insights at Netflix. At Google, she has overseen major platform and UX initiatives, including accessibility-driven design and products for kids and families, positioning her at the intersection of human behavior, product strategy, and the future of AI-driven technology.

More on YouTube related to this episode

• I Asked 5 Tech Leaders What the Tech Job Market Really Wants Now

• Most Engineers Fail Interviews for This Reason

• The Robotics Gap: Hype vs Reality

• Why You Can’t Get Interviews: The Tech Job Market Shift

• AI Engineering: A Realistic Roadmap for Beginners

Connect with me

LinkedIn

Instagram

YouTube

Subscribe now

I went to the RSAC Conference and interviewed 5 tech leaders, and asked them one question: what are you actually looking for when you hire right now? Their answers reveal a massive shift happening in the tech job market that most people aren't talking about.

Whether you're a software engineer, AI engineer, or just starting your tech career, the rules have changed. The skills that got people hired five years agom mastering specific programming languages, memorizing frameworks, are no longer the deciding factor. What companies are hiring for now is completely different, and in this video I break down exactly what that is, straight from the people making hiring decisions.

Watch the full episode 👇

Summary

The shift happening in the tech job market
The job market is moving away from valuing specific tools and programming languages. Hiring is shifting toward adaptability, systems thinking, and the ability to navigate increasing complexity.

What skills matter?
Technical depth alone is no longer enough. The most valuable skills now include problem framing, understanding systems, communication, and the ability to work effectively with AI.

What matters for the job market?
Hiring managers are prioritizing mindset over experience with specific technologies. They are looking for candidates who can think critically, move fast, and understand how their work impacts the broader system.

Is AI replacing jobs?
AI is automating parts of engineering work, especially repetitive tasks. However, it is also creating new roles and increasing demand for people who can manage, guide, and build on top of these systems.

How to build the right skill
Building these skills requires going beyond tutorials and tools. Focus on understanding systems, studying real-world problems, and developing judgment through hands-on experimentation.

Featuring

Mary Carmichael, AI Research Fellow and Member of ISACA

A risk and AI governance expert who has spent 15+ years advising organizations on how to navigate every major technology shift, from digital transformation to the age of agentic AI.

Hom Bahmanyar, Global Enablement Officer at Ridge Security

A computer scientist turned global enablement leader who has watched the engineering skill stack evolve firsthand, and now shapes how security teams worldwide adapt to AI.

Lydia Zhang, President and co-founder of Ridge Security

The co-founder and president of a cutting-edge offensive security company who oversees engineering teams and is actively redefining what she even looks for in a hire as AI rewrites the job.

Shlomie Liberow, founder of Aisy and former head of Hacker R&D at HackerOne

The person who used to find the holes in the world's biggest companies at HackerOne, and now builds AI-powered security tools, someone who has seen both sides of what breaks and what holds.

Jagannadh Mellacheruvu, Head of Product and Policy at Mirror Security

A product and policy leader at the frontier of AI data security, working on the encryption technology that protects what you send to AI, before it even gets there.

More on YouTube related to this episode:

• Most Engineers Fail Interviews for This Reason

• The Robotics Gap: Hype vs Reality

• Why You Can’t Get Interviews: The Tech Job Market Shift

• AI Engineering: A Realistic Roadmap for Beginners

📣✨Connect with me

💻 LinkedIn: https://www.linkedin.com/in/jeanklee/
✉️ Newsletter: https://www.exaltitude.io/newsletter
📲 Email for business inquiries: exaltitude.info@gmail.com
📌 Website: https://www.exaltitude.io/?utm_source=substack
🌸 Instagram: https://www.instagram.com/jeanexplains

Subscribe now

Here are the key lessons:

1/ Leadership role & skills

Leading a major CS department means constant problem-solving and juggling priorities. Leadership today requires managing ambiguity, people, and competing demands.
Success comes from navigating constant change rather than having clear answers upfront.

2/ Key skills in the age of AI

The most important skills are not purely technical but involve judgment and problem framing.
AI can assist, but humans are still needed to interpret, decide, and navigate uncertainty.

3/ Artificial General Intelligence

Predictions about AGI vary widely, but skepticism remains about short-term timelines.
Human intelligence is complex, and current AI is far from replicating it fully.

4/ AI vs CS major

AI and computer science overlap but require different foundations and ways of thinking.
AI focuses more on statistics and data, while CS builds deeper system-level understanding.

5/ Future proofing

The job market is in transition, with uncertainty about which roles will emerge.
Students should focus on strong fundamentals that will remain valuable over time.

6/ AI tools

AI tools are becoming essential, but they should be seen as tools, not replacements.
The real skill is knowing when and how to use them effectively.

7/ Next wave technology

Future breakthroughs will come from combining AI with other fields like healthcare and science.
The biggest opportunities lie in applying AI to real-world problems.

8/ Quantum Computing

Quantum computing has potential, but is still early and searching for practical applications.
Like early AI, it may take time before it reaches widespread real-world impact.

Timestamp:

0:00 Intro

1:20 CS Department Chair

1:35 Leadership skills

2:54 Key skills

4:55 AGI

6:25 AI vs CS

9:50 Future proofing

12:40 AI tools

15:47 Next wave tech

19:40 Quantum Computing

About Nenad

Nenad Medvidovic is a Professor of Computer Science and Chair of the Department of Computer Science at the University of Southern California. He is one of the leading figures in software architecture and helped shape the field into a rigorous engineering discipline. He co-authored Software Architecture: Foundations, Theory, and Practice, a widely used textbook in universities around the world. His research on software systems that evolve while running has had a major impact on modern cloud and large-scale systems. He is an ACM Distinguished Scientist, an IEEE Fellow, and a recipient of multiple major research awards.

LinkedIn: https://www.linkedin.com/in/nenadmedvidovic/

USC profile: https://viterbi.usc.edu/directory/faculty/Medvidovic/Nenad

Resources:

✅ FREE AI ML Roadmap Self Study Plan (16-page PDF Guide)

✅ The Ultimate Resume Handbook

✅ FREE ATS-Friendly Resume Template

More on YouTube related to this episode:

• What AI Actually Means for Software Engineers

• Most Engineers Fail Interviews for This Reason

• The Robotics Gap: Hype vs Reality

• Why You Can’t Get Interviews: The Tech Job Market Shift

• AI Engineering: A Realistic Roadmap for Beginners

What happens to software engineering when AI can now write code?

In this conversation with Gergely Orosz, creator of Pragmatic Engineer and author of The Software Engineer’s Guidebook, we break down what is actually changing and what is not.

Gergely shares what it was like working at Uber when “it was the hottest company on the planet,” and why “from the outside it will look great and everything is held together by duct tape.” We talk about the gap between perception and reality inside fast-growing tech companies, and what engineers can learn from that.

We also go deep on AI and hiring. Until recently, coding tests mattered because “most people will give up.” Now “AI comes in, and everyone can do this.” So what differentiates engineers going forward?

Here are the key lessons:

1. Time at Uber
High-growth companies can look polished but be fragile internally. Speed often comes with technical debt and tradeoffs. The perception of success is often very different from the day-to-day reality.

2. Patterns of successful engineers
Strong engineers are product-minded, not just code-focused. Curiosity and initiative differentiate top performers. High-quality work and proactive thinking compound over time.

3. Skills for a Manager
Technical credibility is essential for effective leadership. Managers need to understand the work, not just coordinate it. Balancing people and technical decisions is key.

4. Trying new things
Career growth comes from experimenting, not just optimizing. Trying new roles or paths builds a broader perspective. Progress often requires stepping into discomfort.

5. AI writing code
AI is rapidly changing how code is written. Coding is becoming more accessible and less of a bottleneck. The role of engineers is shifting beyond just writing code.

6. Becoming an expert
Expertise still requires time and sustained effort. AI does not replace deep understanding. Long-term success comes from mastery, not shortcuts.

7. AI Tools
AI tools lower the barrier to entry for many tasks. They change workflows but not the need for judgment. Engineers must learn to use AI effectively, not rely on it blindly.

8. Career Trends
Teams are becoming smaller and more efficient. Some traditional roles are becoming less common. The industry is shifting toward higher leverage work.

9. Career Advice Myth
Traditional signals like tests and credentials are weakening. Grit and persistence used to be key differentiators. AI is changing what signals matter in hiring.

10. SWE in 5 years
The role of engineers will continue evolving with AI. Expectations will increase as tools improve. Adaptability will be critical for staying relevant.

11. Will AI replace SWE?
AI will not eliminate the need for engineers. Building reliable systems remains a human responsibility. The role will change, but it will not disappear.

⏱️Timestamp:
==============
0:00 Trailer
1:05 Origin story
3:42 Time at Uber
9:45 Manager transition
14:55 patterns of successful engineers
17:18 Skills for Manager
19:58 Trying new things
22:30 AI writing code
25:20 Becoming an expert
26:40 AI Tools
29:58 Career Trends
31:39 Career Advice Myth
34:30 Job hopping
36:18 SWE in 5 years
39:42 Rapid fire
42:04 Will AI replace SWE?

Watch the full episode here:

About Gergely

Gergely Orosz is a software engineer, engineering leader, and the creator of The Pragmatic Engineer, the #1 software engineering newsletter on Substack. He previously worked at companies like Skype and Uber, where he experienced both large-scale systems and hypergrowth environments firsthand. Through his writing, podcast, and his book The Software Engineer’s Guidebook, he shares practical, experience-driven insights on software engineering, careers, and how the industry is evolving.

Substack: https://newsletter.pragmaticengineer.com/
Book: https://www.engguidebook.com/
LinkedIn: https://www.linkedin.com/company/the-pragmatic-engineer/

More on YouTube related to this episode:

• Most Engineers Fail Interviews for This Reason

• The Robotics Gap: Hype vs Reality

• Why You Can’t Get Interviews: The Tech Job Market Shift

• AI Engineering: A Realistic Roadmap for Beginners

Like many engineers who have worked inside large tech companies, she has seen the interview process from both sides. What stood out most in our conversation was something many candidates misunderstand.

Being a strong engineer is not just about knowing how to solve a problem.

And technical interviews are not just about arriving at the correct solution.

There is something else interviewers are evaluating the entire time.

💡 Watch the full video episode here.

Being a Good Engineer Is Not Just About Solving Problems

Jean: Many people preparing for technical interviews focus almost entirely on solving problems correctly. But you mentioned something interesting earlier. Being a good engineer is not just about knowing how to solve a problem. What do you mean by that?

Maddy: I think this is one of the biggest misconceptions about technical interviews.

People think the goal is to arrive at the correct answer. But interviews are not really about the answer. They are about how you get there.

In a real engineering environment, you are rarely working alone. You are collaborating with teammates, discussing ideas, and evaluating tradeoffs.

Interviewers are trying to simulate that dynamic.

So they are watching:

• How you think.

• How you communicate.

• How you explore different approaches.

If you jump straight to a solution without explaining your reasoning, that can actually work against you.

Jean: So the interview is less like a test and more like a collaborative exercise?

Maddy: Exactly. Think of it as problem-solving with another engineer.

The interviewer is not just checking if you can code. They want to see how you reason through uncertainty.

Why She Left Google

Jean: You spent several years at Google. Why did you decide to leave?

Maddy: When I decided to leave, it was really for three primary reasons.

The first was that I was getting interested in working on a different kind of product.

Google is an incredible company, but it is also very large. Sometimes it can take a long time for things to move.

I wanted to work somewhere where I could see a more direct connection between the work I was doing and the product experience.

The second reason was growth.

At some point in your career, you want to challenge yourself in new environments. Different companies have different engineering cultures, and that can change how you think about building systems.

And the third reason was curiosity.

I wanted to experience another company that operates at large scale but with a different structure.

Jean: Was compensation part of the decision?

Maddy: Not really.

Actually, Maddie and I had a bit of a conversation about that because people often assume Google pays more than almost anyone else.

But when I looked up the numbers, it was not as simple as that.

Compensation varies a lot depending on the role, level, and company.

And honestly, after a certain point, other factors start to matter more.

The Language of Engineering

Jean: You said when you started, you did not even know the language. Let’s talk about the myth of learning languages.

Maddy: When I say language, I do not necessarily mean a programming language.

I mean the language of engineering.

When you first enter the industry, there are so many things you do not know.

You do not know the systems.

You do not know the internal tools.

You do not know the conventions.

You do not know the expectations for how engineers communicate.

And that can feel overwhelming.

You are learning how the system works. How decisions are made. How problems are discussed.

It is almost like learning a new culture.

And once you understand that language, everything becomes much easier.

The Role of AI in Engineering

Jean: Let’s talk about AI.

A lot of engineers are wondering how tools like AI coding assistants are going to change their jobs. How do you see it?

Maddy: AI definitely makes certain things faster.

For example, if you are writing boilerplate code or trying to remember a specific syntax, AI can help you generate that very quickly.

It can also help you explore ideas faster.

But I do not think it replaces the core skill of engineering.

Jean: Which is?

Maddy: Knowing what problem to solve.

AI can generate solutions, but it still needs direction.

You still have to understand the system you are building. The constraints. The tradeoffs.

Those decisions are very hard to automate.

Jean: So AI changes how engineers work, but not necessarily what they do.

Maddy: Exactly.

It changes the workflow.

But understanding systems, architecture, and product decisions is still very prioritized.

What Interviewers Are Actually Evaluating

Jean: Let’s go back to interviews.

If interviews are not just about arriving at the correct solution, what are interviewers actually evaluating?

Maddy: There are a few things.

First is your reasoning process.

Interviewers want to see how you approach the problem. Do you clarify assumptions? Do you think about edge cases?

Second is communication.

Can you explain your thinking clearly?

In real engineering work, you spend a lot of time explaining decisions to other people.

If you cannot communicate your reasoning, it becomes very difficult to collaborate.

Third is adaptability.

Sometimes the interviewer will introduce new constraints or ask follow up questions.

They want to see how you respond.

Do you panic, or do you adjust your approach?

Jean: So it is much more dynamic than people expect.

Maddy: Yes. It is a conversation.

The Mistake Many Candidates Make

Jean: What is the biggest mistake you see candidates make during interviews?

Maddy: Probably staying silent while thinking.

Many candidates feel pressure to produce the correct solution, so they stop talking and try to solve everything internally.

But from the interviewer’s perspective, that creates a problem.

We cannot see how you are thinking.

If you explain your reasoning out loud, we can follow your thought process.

Even if your approach is not perfect, it shows how you think.

Why AI Does Not Eliminate Interviews

Jean: Some people argue that AI might make coding interviews obsolete.

If AI can solve algorithm problems instantly, what is the point of testing them?

Maddy: I think interviews will evolve, but they will not disappear.

Because interviews are not actually about memorizing algorithms.

They are about reasoning.

Even if AI helps you write code faster, engineers still need to understand systems and make decisions.

Companies still need to evaluate those skills.

So the format might change, but the underlying goal will remain.

Preparing for Interviews in the AI Era

Jean: How should engineers prepare for interviews today?

Maddy: Practice is still important.

Working through algorithm problems helps you develop intuition about patterns and complexity.

But I would also focus on explaining your thinking.

When you practice problems, do it out loud.

Imagine you are explaining your reasoning to another engineer.

That skill translates directly into interviews.

Jean: People spend so much time solving problems, but not necessarily explaining them.

Maddy: Exactly. And communication is one of the most underrated skills in engineering.

The Future of Engineering Work

Jean: Looking ahead, what skills do you think will matter most for engineers?

Maddy: I think systems thinking will become more important.

AI tools make it easier to generate code, but designing systems is still a complex human task.

Understanding architecture, tradeoffs, and scalability will matter a lot.

And collaboration will matter even more.

Engineering is becoming increasingly interdisciplinary.

You are working with product managers, designers, data scientists.

Being able to communicate across those roles is incredibly valuable.

Final Advice for Engineers

Jean: If you could give one piece of advice to engineers preparing for interviews today, what would it be?

Maddy: Focus on the process, not just the answer.

Technical interviews are not a puzzle competition.

They are a way for companies to understand how you think and collaborate.

If you treat the interview like a conversation rather than a test, you will perform much better.

About Maddy

Maddy is a Senior Software Engineer. She spent over four years at Google, where she worked on Google Ads retail experiences and infrastructure for Google Search, including health and image viewer systems. Earlier in her career, she completed engineering internships at Amazon, Microsoft, IBM, and Morgan Stanley, working on projects ranging from Alexa search recommendations and developer tooling to data infrastructure and analytics systems.

Maddy's Instagram

More on YouTube:

• The Robotics Gap: Hype vs Reality

• Why You Can’t Get Interviews: The Tech Job Market Shift

• AI Engineering: A Realistic Roadmap for Beginners

It’s 2026, and every year around this time, I revisit my goals and my vision.

Last week, I pulled out a goal-setting workshop I did on January 24, 2015.

I got chills reading my old worksheets.

Back then, I wrote that my vision was to research retirement plans that would allow me to add value to the world by helping others.

I also wrote some goals, and one of them was to learn storytelling.

I’m deeply grateful to say this out loud: I am living the life I was dreaming about.

2015

For context, let’s go back to 2015. I was the 19th Software Engineer at WhatsApp. WhatsApp had just been acquired in 2014 by Facebook for $19 Billion.
By January 2015, I was suddenly a multimillionaire overnight.

I still had three more years of vesting ahead of me, but I already knew something that scared me more than work ever had.
One day soon, I would never have to work for money again.

I was 29 years old.

And the question that haunted me was simple and terrifying:
What do I do with myself after that?

During that workshop, I learned a framework that stuck with me for over a decade.

Vision is your positive dream for your life, usually looking one to three years out.
Goals are the specific, ordered steps that move you toward that vision.
Milestones are the small, concrete actions that make the goals real.

There were three types of goals we were taught to write.

First, goals with a specific date and wording.

Examples:
Vision: I want financial stability in my life.
Goal: By a specific date, I will be working in a new job earning no less than a specific amount.

Second, goals based on frequency and clarity.

Examples:
Vision: I want to give back to my community.
Goal: At least twice a month, I will serve food to the unhoused.

Third, goals with frequency plus accountability.

Examples:
Vision: People can count on me.
Goal: I am a person who arrives five minutes early to appointments.

I also scribbled down a few notes.

If you don’t plan a month ahead, you tell yourself you don’t have time.
If you’re scared, take small steps, or you freeze yourself.
Don’t be too scared.

I had completely forgotten about this workshop, but I believe it stayed somewhere in my subconscious.

2026

I just hit 100K subscribers on YouTube in January, and I wrote a post about how close I came to stopping myself from ever posting there at all.

Because I had every single one of the six reasons we were warned about.

1. Feeling too shy.

2. Feeling unworthy.

3. Doubting my ability.

4. Believing I didn’t have time.

5. Assuming someone else was better than me.

6. Afraid to be seen in public.

I didn’t want to admit it, but it was all true.

I was shy to share my thoughts.
I didn’t feel my opinions mattered.
I doubted I could do it.
The time commitment felt overwhelming.
There were so many people who seemed better than I was.
And I was afraid to speak up publicly.

The 2015 version of me would be so proud of the 2026 version of me. Not because of the subscriber count, but because I had the courage to follow my vision.

2026 Intentions

Following your vision also means being intentional about how you build things.

I did the goal-setting exercise again this January 2026, and I realized something. While the vision hasn’t changed, the way I want to create and sustain this work has.

Over the past year, I’ve been thinking carefully about how I write, share, and show up long term.

So here are my intentions:

• I’ll continue making YouTube videos, including deeper interviews and shorter, timely updates on Instagram and TikTok.

• I’ll keep sharing ideas and resources in smaller, more frequent posts on LinkedIn.

• I’ll continue experimenting with formats that make this work more accessible, beyond just video.

What matters most to me is independence and integrity.

I only take sponsorships from companies I would genuinely use myself, after doing real diligence on the product and the people behind it. I also don’t blur the line between paid and unpaid recommendations, and I’m explicit when something is sponsored.

The due diligence process is slow and limiting, and I’m okay with that.

Every dollar earned goes straight back into building better content through editors, tools, and support.

This brings me to one practical change.

I currently write to over 22,000 people on MailerLite, and maintaining this list costs over $2,000 a year, and the cost only keeps going up as the newsletter grows. To simplify things and make long-form writing more sustainable, I’ve decided to migrate the newsletter to Substack.

FREE 1-month of Paid Subscription

Before that transition, I wanted to do something for you.

I’m giving away a free 1-month trial to everyone who’s subscribed to me up to this point. You’ll get access to everything I’ve created so far, including resume templates, AI learning guides, job search resources, interview prep, and more.

You’ll have access for the next 30 days during the transition. Feel free to download any resources during this time.

If these resources have been helpful, sharing this newsletter with a friend is one of the simplest ways to support this work.

Final challenge

If any of these fears are quietly stopping you from doing something you value
being too shy, feeling unworthy, doubting your ability, thinking you don’t have time, believing someone else is better, or being afraid to be seen, I hope you find the courage to start today.

You don’t have to do anything grand.

Take one small step.
Write one sentence.
Spend five minutes with your coffee and write down a vision and one goal.

If you do, and you feel like sharing, post it on LinkedIn.
If you tag me, I’ll comment to support.

I’m rooting for your 2036 self!

Best,
Jean

An ATS is a software that big tech companies use to streamline their hiring process. If your resume isn’t optimized for it, it could be filtered out before it even reaches human eyes.

Why do companies use ATS?

To save time! When hundreds of resumes flood in for a single role, the ATS helps sort through them quickly, only passing along resumes that fit the criteria.

Companion Video

How to Make Your Resume ATS-Friendly

Below are the do’s and don’ts to make sure your resume isn’t filtered out automatically.

Use the PDF template below as a baseline for optimal formatting.

Read more

Recruiters are overwhelmed with the volume of resumes they get and don’t have time to review every single one of them. If your resume has random keywords or irrelevant information, it’s straight to the “No” pile.

Think of it like a menu for a tourist restaurant, with sushi, hot pot, burgers, and pizza–does it sound like a quality restaurant?

Customize your resume for the job, ditch the random skills, and focus on your best projects relevant to the role you’re applying to! Be the Jiro, the three-Michelin-starred sushi restaurant of resumes.

A tailored resume grabs attention. Use the keywords the employers use in the job descriptions and stand out from the crowd!

— From page 9 of The Ultimate Resume Handbook.

Keyword Resources to get you started

To help you get started, I’ve compiled a list of key terms for twelve popular job titles: AI Engineer, Web Developer, Software Engineer, Back-End Engineer, Front-End Engineer, iOS Developer, Android Developer, Machine Learning Engineer, Data Engineer, Data Scientist, Data Analyst, and Business Analyst. We looked at 257 job postings and found the top 10 keywords used the most, showing the percentage of times each keyword appeared. For example, in all AI Engineer job postings, 66% included the words “Python.”

Check out the patterns of the lists: AI engineering and Web Developer jobs have fewer common keywords across postings. On the flip side, Front-End Engineer roles really want React (92%). This is because AI and software engineering roles cover a broader range of expertise, needing diverse skills, whereas Front-End jobs are more specific. Also, Web Developers usually work on both Front-End and Back-End, requiring a mix of both skill sets.

That’s partly also why the Front-End jobs are getting replaced by AI faster than the other jobs.

Let’s dive into the keywords list!

Companion Video

Read more