Location: San Francisco, CA, United States Job Type: Full-time Salary: $150K - $200K Experience: Any (new grads ok) Visa: Will sponsor Nori Robotics (YC S26) builds affordable humanoids whose intelligence scales with our fleet. We are looking for an engineer who can connect reliable LLM agents with working robot behavior. Scope: Build agents that turn task descriptions into robot policies, improve them through simulation and execution feedback, and deploy them as a customer-facing service. What you will work on • Design model-facing tools and APIs, multi-step agent loops, and reusable skill libraries. • Generate and test robot policies in simulation, using execution feedback, context management, and retry budgets to recover from failures. • Build reproducible evaluations, held-out tasks, and regression tests that verify success independently of model self-reports. • Serve concurrent agents, safely isolate generated code, and deploy tested policies to physical robots while tracking latency, reliability, and compute cost. Requirements • Strong Python and experience building LLM agents that run unattended over multiple steps. • Demonstrated experience deploying and debugging manipulation, imitation-learning, or control policies on physical robots, with measurable results. • Strong robotics fundamentals: kinematics, coordinate frames, feedback control, calibration, and actuator limits. Ability to diagnose failures across policies, sensors, and hardware using ROS 2 or equivalent. • Experience with Isaac Sim/Lab, MuJoCo, SAPIEN, or equivalent, including contact dynamics and sim-to-real discrepancies. • Experience operating GPU-backed services with queues, retries, cancellation, and cost controls; familiarity with sandboxing, network restrictions, and resource limits for generated code. Nice to have Research in agents, program synthesis, or robot learning; PyTorch and LeRobot; multi-agent orchestration; on-device inference and power profiling; fleet deployment and OTA updates. San Francisco, CA. Full time. In person. Compensation. Base pay: $150,000-$200,000 per year, depending on job-related knowledge, skills, and experience.
Summary
Build LLM agents that turn task descriptions into deployable robot policies, improving them through simulation and execution feedback. Requires strong Python, multi-step unattended agents, ROS 2 debugging, and experience with Isaac Sim, MuJoCo, or SAPIEN and sim-to-real transfer.
Responsibilities
Design model-facing tools and APIs, multi-step agent loops, and reusable skill libraries; generate and test robot policies in simulation using execution feedback and retry budgets; build reproducible evaluations and regression tests; serve concurrent agents and deploy tested policies to physical robots
Qualifications
Strong Python and experience building LLM agents that run unattended over multiple steps; demonstrated experience deploying and debugging manipulation, imitation-learning, or control policies on physical robots; strong robotics fundamentals: kinematics, coordinate frames, feedback control, calibration, and actuator limits