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.