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.
