This as told essay is based on a conversation with Ruiyu Li, a 26-year-old AI researcher at Meta, based in Menlo Park. Her identity, employment and salary (including base, bonus and equity) have been confirmed by Business Insider. This story has been edited for length and clarity.
I am a research scientist at Meta, specialized in AI and machine learning.
I work on training AI models to power large-scale recommendation systems, building AI-native agents to improve efficiency, and optimizing LLM post-training and inference.
I graduated from college in 2022 and completed my masters in machine learning at Carnegie Mellon University in 2023. It was a difficult time on the technical labor market. I thought career fairs and conferences were useless, but I ended up getting three Big Tech offers. I first went to Microsoft, but later ended up at Meta.
That experience gave me practical lessons on what actually helped get technology deals in a tough market.
I had a lot of work to show
If you want to work in AI, you need to be familiar with algorithms and coding. AI helps people do a lot of work, but the fundamental knowledge is still necessary.
You should have that too results to show to employers. I encourage people looking to enter the field to gain as much hands-on AI experience as possible so they can adapt as the field rapidly evolves. I recommend interning at a startup or publishing research.
I interned at a startup in college and at Microsoft during high school, and both required me to bring AI innovations into production. When I interned at the startup, there were less than ten people and I worked directly with the CEO and CTO.
When I look at candidates, I look to see if they have research and projects. In a competitive job market, it is not enough to simply list experience or projects on your resume. If you have published articles or an open source codebase for employers to look at, that will be more useful.
Before joining Meta, I conducted academic research in eight AI labs in various areas of machine learning. At Meta, I applied that foundation to production AI systems, where model quality must be balanced with scalability, reliability, efficiency, and measurable impact in the real world.
The more you do, the more practical experience you gain and the faster you can learn and adapt to AI trends.
I focused on a niche
If you want specifically working in AII recommend going to graduate school.
After my studies, I decided that I wanted to work more on applied AI research. I have a bachelor’s degree in computer science, but I wanted to delve deeper into it. Mine master’s program had many cutting-edge projects that were practical, and that allowed me to be part of groundbreaking research.
My internship experiences were also very important in finding a full-time job. Many employers want to see technical experience and solid projects at the sector level.
When I interned at a startup, I had the opportunity to work directly on video editing automation using AI innovations, which were state-of-the-art at the time. During my internship at MicrosoftI worked on generative search and Bing Copilot, delivering an end-to-end video generation pipeline using a multimodal LLM shipped to production at Bing Search.
In both internships I developed a niche by focusing on large-scale AI systems in production environments and AI video generation. I During my internship I developed many transferable skills and experience that transferred into my full-time role.
Do you work in Big Tech? We would like to hear how you got your job. Contact the reporter at aaltchek@insider.com.
