Research Scientist

Connect with me and feel free to apply if you’re a Researcher or Machine Learning Engineer and aren’t sure you’re the perfect fit. I’m currently working with two elite, well-funded teams in the Bay Area (SF & Palo Alto) that are taking Reinforcement Learning in polar opposite, but equally ambitious, directions: 1. The Foundational Research Lab (San Francisco) Founded by "old-school" RL PhDs, this lab believes the field is scaling prematurely while ignoring core issues like data inefficiency and long action horizons. Backed by Vercel and South Park Commons, they are building a research-driven environment to scale RL-LLM hybrids by orders of magnitude. The Mission: Moving past DPO/RLHF to create agents that genuinely generalize. The Team: Talent from DeepMind, Meta, and NVIDIA. 2. The "Ground Truth" Reasoning Startup (Palo Alto) A Stanford spinout solving for long-horizon reasoning by moving RL into a space where physics and logic provide a non-negotiable ground truth: Chip Design. They are building a "Cursor for Verilog" where agents must plan, critique, and verify their own code against real execution feedback. The Mission: Collapsing the 3-year hardware design cycle through automated reasoning. The Team: Led by the former Head of AI (Trust & Safety) at Anthropic, with peers from xAI and OpenAI. The Backing: Top-tier VCs with support from figures like Jeff Dean. Both teams are looking for "founding-level" engineers who can ship production-grade systems, not just run experiments. If either of these philosophies: foundational scaling or physical verification align with where you want to take your next career move, feel free to apply.