How a 26-year-old Meta researcher broke into Big Tech

Li’s path to Menlo Park began with a Master’s in machine learning from Carnegie Mellon University. While many peers relied on traditional hiring channels, she found them largely ineffective during the tech downturn. Instead, she focused on building a verifiable track record, completing internships at a small startup and Microsoft. At the startup, working directly with leadership allowed her to push AI innovations into production, while her tenure at Microsoft involved developing generative search tools and video pipelines for Bing Copilot.

Today, Li focuses on training recommendation systems and optimizing LLM inference. She argues that in a competitive landscape, a résumé alone is insufficient. Employers look for open-source contributions or published papers that demonstrate a candidate’s ability to handle the trade-offs between model quality, scalability, and real-world efficiency. For those aiming to enter the field, she suggests that graduate-level education combined with niche-focused internships remains the most reliable strategy for gaining the technical authority required to land a role at a top-tier firm.