Acceler8 Talent
Senior Research Scientist (San Francisco)
Acceler8 Talent, San Francisco, California, United States, 94199
Senior Research Scientist - San Francisco or Redwood City
A company who are building AI systems with general physical ability are looking for Senior Reinforcement Learning Research Scientist to build reinforcement learning systems that drive real-world experimentation and tool orchestration.
What Will I be Doing:
Create and maintain RL environments for optimizing tools, experiments, and process workflows using both simulations and digital twins. Develop safe RL strategies, reward structures integrated with verifiers, and frameworks for moving from offline training to robust online behavior. Define state and action representations and enforce constraints for reliable, long-horizon decision-making. Partner with LLM researchers, agent engineers, and simulation teams to integrate RL agents into live experimental systems.
What We're Looking For:
Expertise in reinforcement learning, optimal control, or sequential decision-making, with experience applying RL to complex real or simulated systems. Familiarity with safe RL, constrained RL, verifier/detector integration, or multi-step policy evaluation frameworks. Demonstrated ability to build RL environments, design reward structures, and diagnose policy behavior at scale. Comfortable working across ML, simulation, systems engineering, and physical-toolchain interfaces in a fast-paced research environment.
Whats in It for Me:
$200-$300k and meaningful equity Ownership of cutting-edge RL systems deployed in real-world experimental workflows Work at the cutting edge of AI, robotics, and experimental science Contribute to building the worlds first physical superintelligence
Apply now for immediate consideration!
A company who are building AI systems with general physical ability are looking for Senior Reinforcement Learning Research Scientist to build reinforcement learning systems that drive real-world experimentation and tool orchestration.
What Will I be Doing:
Create and maintain RL environments for optimizing tools, experiments, and process workflows using both simulations and digital twins. Develop safe RL strategies, reward structures integrated with verifiers, and frameworks for moving from offline training to robust online behavior. Define state and action representations and enforce constraints for reliable, long-horizon decision-making. Partner with LLM researchers, agent engineers, and simulation teams to integrate RL agents into live experimental systems.
What We're Looking For:
Expertise in reinforcement learning, optimal control, or sequential decision-making, with experience applying RL to complex real or simulated systems. Familiarity with safe RL, constrained RL, verifier/detector integration, or multi-step policy evaluation frameworks. Demonstrated ability to build RL environments, design reward structures, and diagnose policy behavior at scale. Comfortable working across ML, simulation, systems engineering, and physical-toolchain interfaces in a fast-paced research environment.
Whats in It for Me:
$200-$300k and meaningful equity Ownership of cutting-edge RL systems deployed in real-world experimental workflows Work at the cutting edge of AI, robotics, and experimental science Contribute to building the worlds first physical superintelligence
Apply now for immediate consideration!