Principal Robotics Learning Engineer

Responsibilities

• Build and evaluate proof-of-concept autonomy skills and establish measurable performance baselines.
• Define architecture and interfaces across data, simulation, training, policy inference, controls, and evaluation.
• Develop, train, evaluate, and deploy reinforcement-learning, behavior-cloning, and imitation-learning policies.
• Lead cross-team execution, resolve technical blockers, mentor engineers, and implement critical system components.
• Build data collection, synchronization, curation, quality-checking, versioning, and reproducible training tools.
• Transfer policies from simulation to physical machines while addressing sensor noise, latency, action limits, inference performance, and changing dynamics.
• Integrate learned policies with perception, state estimation, controls, runtime supervision, fallbacks, and operating-mode transitions.
• Establish evaluation, regression testing, failure diagnosis, and controlled hardware validation for reliable field deployment.

Requirements

• Staff-level or above technical ownership of substantial robotics or robot-learning efforts, including technical direction, architecture decisions, cross-team coordination, and delivery of working systems.
• Demonstrated personal implementation, training, deployment, and evaluation of learned policies on physical robots or vehicles, with depth in reinforcement learning, imitation learning, or behavior cloning.
• Recent hands-on contributions to production or deployment-quality software, learning experiments, and hardware debugging.
• Experience mentoring engineers and improving technical decisions through design and code reviews.
• Strong understanding of machine learning, optimization, sequential decision-making, generalization, and distribution shift.
• Proficiency in Python and a modern learning framework such as PyTorch or JAX.
• Experience building reproducible data, training, and evaluation workflows and interpreting experimental results.
• Working knowledge of robotics, including observations, actions, dynamics, feedback, and physical constraints.
• Ability to write maintainable software, diagnose failures across system boundaries, and communicate technical tradeoffs clearly.
• Degree in Robotics, Computer Science, Electrical or Mechanical Engineering, or a related field, or equivalent practical experience.
• Preferred experience includes sustained field or customer deployment, demonstration collection, teleoperation, dataset design, offline reinforcement learning, policy fine-tuning, simulation-to-reality transfer, system identification, domain randomization, visuomotor or diffusion policies, hierarchical or multitask skill learning, C++, real-time robot software integration, and robotics applications involving variable loads or contact conditions.