Job Description

💡 What if your next role let you design reinforcement learning systems that directly control real-world science —training agents that run experiments, tune physical tools, and accelerate discovery from months to days?

🚀 Our client — a hyper–talent-dense physical-AI startup backed by top-tier builders across frontier AI and deep tech — is hiring a Senior RL Research Scientist (San Francisco / Redwood City, In-Person).

They’re building Physical Superintelligence : AI that can ask any scientific question about the physical world and get a real answer in days, not years—by pairing large models with massive automated “experiment factories.”

The role:

🎯 Own RL for real-world tool control and long-horizon workflows across biology, chemistry, materials, and robotics

🧪 Build RL environments grounded in physics, simulation, and digital twins —not toy benchmarks

🛡️ Design safe / constrained RL + verifier-integrated rewards to keep agents reliable in high-stakes domains

📈 Drive offline → online transitions and evaluation pipelines as agents learn from and act on real telemetry

🤝 Partner across LLM, agent systems, simulation, and physical-tooling teams to deploy policies into production workflows

⚙️ High-ownership algorithm + systems work where you shape the foundations of physical-AI autonomy

Why join?

🌍 Work at the frontier of real-world impact —from multi-cancer early detection to general scientific discovery

🏭 Operate on a uniquely scaled platform : 20k+ sq ft automated facilities producing real experimental feedback loops

💰 Competitive salary + meaningful equity

🚀 Tiny, world-class team with a “best-person-you’ve-worked-with” talent bar

🏢 In-person, high-velocity environment with deep collaboration across SF + Redwood City

📩 Interested? Apply now

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