Applied AI Engineer, Agents & Robot Learning
Nori Robotics
San Francisco, California, United StatesFull-time$150,000–$200,000Posted Oct 19, 2026
About the role
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.
Responsibilities
- Build LLM agents, tools, APIs, and reusable skills that translate task descriptions into robot policies.
- Generate and improve robot policies using simulation and execution feedback.
- Develop evaluations and regression tests for robot task success.
- Deploy agents and tested policies to physical robots, tracking latency, reliability, and compute cost.
Qualifications
- Strong Python skills and experience building unattended, multi-step LLM agents.
- Experience deploying and debugging manipulation, imitation-learning, or control policies on physical robots with measurable results.
- Strong robotics fundamentals, including kinematics, coordinate frames, feedback control, calibration, and actuator limits.
- Ability to diagnose policy, sensor, and hardware failures using ROS 2 or equivalent.
- Experience with a robotics simulator such as Isaac Sim/Lab, MuJoCo, or SAPIEN, including contact dynamics and sim-to-real discrepancies.
- Experience operating GPU-backed services and familiarity with sandboxing and resource controls for generated code.
- Nice to have: experience in agent research, program synthesis, robot learning, PyTorch, LeRobot, multi-agent orchestration, on-device inference, or fleet deployment.
Skills mentioned
PythonAI AgentsLarge Language ModelsRoboticsPyTorchReinforcement LearningDistributed SystemsModel DeploymentDockerSoftware Testing