This post was originally published on this site.
This past weekend, I had the incredible opportunity to speak at the University of Washington on a panel titled “Developers in the AI Era.”
It was a great experience speaking to students who are eager to break into AI.
One thing that stood out to me was how early students are getting involved in AI. Many of them were already working on AI-related projects, training models, and exploring tools even before graduating.
Seeing their enthusiasm reassured me that the next wave of engineers aren’t actually “cooked”—despite what social media might suggest.
Here is the list of questions I got asked:
1. What motivated you to enter this industry, and how did you navigate your career path?
2. How has AI transformed your industry in recent years?
3. Do you see AI more as a challenge or a tool in your career?
4. Can you share your professional experience where AI made a significant impact?
5. How do you balance AI automation with the need for human oversight?
6. As more and more AI starts to be integrated into software development, what technical skills are most valuable for us to adapt to the transition?
7. How should we prepare for the potential displacement of certain jobs in tech due to AI automation?
8. For each panelist, any advice for our students at UW? Anything else you would like to share?
I’ll do my best to summarize my answers into this small excerpt.
The moment I knew I needed to transition into AI came during my time at TikTok. I was working on a new feature for TikTok Shop and got to collaborate with the Algorithm Team, which included machine learning engineers and data scientists responsible for the FYP recommendation system we all use*.
As a result of my work, TikTok Shop content was dynamically promoted to users’ FYP feeds using a tiering algorithm based on engagement signals and user data. This experience gave me a firsthand look at AI-driven ranking, recommendation systems, and large-scale ML deployments.
This was an invaluable experience, as TikTok serves billions of users globally and is one of the most sophisticated recommendation systems in the world.
My background and industry experience were rooted in backend engineering and distributed systems, which gave me a strong foundation in scalability, infrastructure, and software engineering best practices. I firmly believe that one of the biggest advantages of working at a big tech company is the opportunity to build and optimize systems at scale.
Developing products that serve millions (or billions) of users presents challenges and insights that are fundamentally different from those encountered at smaller companies. I don’t believe I’d be as valuable as an engineer today without first learning large-scale system design and backend engineering.
Given my background and my unique experience at TikTok, I felt I was ready for this next step in my career. AI has dominated the tech landscape since ChatGPT launched in November 2022—an event that, in hindsight, feels like a lifetime ago.
I want to emphasize that you shouldn’t switch to AI just because it’s the current hype. Before AI, we saw similar hype cycles around Web3, Crypto, NFTs, Blockchain, and the Metaverse. You should really be certain that this is a field you can see yourself enjoying because there is certainly a higher bar when compared to standard engineering roles.
My detailed advice for anyone trying to break into AI is laid out in a concise roadmap here. To summarize, you need to learn the basics of Python, Math, and ML fundamentals. This will give you the basic skillset to start contributing to applications that use AI.
I’d strongly emphasize the importance of building projects in your AI journey. The best way to learn anything is by doing. Work on real-world problems, or, better yet, gain real industry experience. If you don’t have experience in AI, the most impressive thing you can add to your resume is a project you built from scratch and speak about in technical depth.
Additionally, I want to emphasize that you aren’t too late to this field. Opportunities to grow as an engineer and advance your career in AI are just beginning. Whether you’re a newcomer or an experienced engineer looking to pivot, there has never been a better time to enter AI.
If you’re already a working professional, for example a software engineer in the industry, I highly recommend seeking internal opportunities to pivot into AI-related projects. Most companies, particularly in big tech, have internal mobility programs that allow employees to transition to different teams.
I’ve personally leveraged this at nearly every company I’ve worked for, using internal transfers to move into higher-impact products and teams that aligned better with my skills and career goals. If you’re looking to break into AI, starting within your current company can be one of the most effective and strategic moves. It can be much less daunting than having to grind LeetCode, prepare for system design interviews, and go through a grueling hiring process, especially if you are happy at your current company.
*Fun fact: I never used TikTok until I started working there and had to debug my code.
Developers are a crucial part of the AI engineering pipeline and won’t be replaced anytime soon. My goal is to help more engineers break into AI/ML.
The next generation of developers will not only use AI, they will redefine what software engineering looks like in the AI era.
I’ll be sharing more AI career insights, technical deep dives, and industry trends here on The AI Engineer. Subscribe to stay updated!