N
Nori Verified
Climate Tech
Applied AI Engineer, Agents & Robot Learning
San Francisco, CAOnsiteFull Time$150,000–$200,000 /yrPosted 19 days agoVisa sponsorship availableHidden Gem · YC Startup
Required skills
- Python
- LLM agents
- robot policies
- simulation
- execution feedback
- retry budgets
- reproducible evaluations
- regression tests
- GPU-backed services
- queues
Nice to have
- agents
- program synthesis
- robot learning
- PyTorch
- LeRobot
- multi-agent orchestration
Role summary
Nori Robotics is seeking an Applied AI Engineer specializing in Agents & Robot Learning to build and deploy intelligent humanoid robots. The role involves developing LLM agents that translate task descriptions into robot policies, refining them through simulation and real-world feedback, and ensuring their reliability and scalability. You will design agent loops, APIs, and skill libraries, implement robust testing and evaluation frameworks, and manage the deployment of policies to physical robots, focusing on performance metrics like latency, reliability, and cost. This is an onsite, full-time position in San Francisco.
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.
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.
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