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AIM Intelligent Machines builds autonomy systems for heavy equipment, turning earthmoving machines into intelligent robots that adapt to changing terrain, materials, and loads. We are seeking a Principal or Staff-level Robotics Learning Engineer to lead the technical development of data-driven autonomy skills, enabling machines to learn and improve from demonstrations, simulation, and real-world experience.
In this hands-on individual-contributor role, you will own the end-to-end technical effort to develop learned autonomy skills for heavy equipment applications such as digging, loading, and grading. Your responsibilities include:
**Technical Leadership & Architecture**
Lead the technical effort from proof-of-concept through deployment. Build and evaluate a POC for a representative autonomy skill to establish feasibility and measurable performance baselines. Use findings to identify gaps in data, simulation, learning methods, machine integration, and evaluation. Develop and execute a phased technical roadmap addressing critical gaps. Define architecture and interfaces across data, simulation, training, policy inference, controls, and evaluation. Make and communicate tradeoffs between RL, imitation learning, behavior cloning, and established robotics methods based on task needs and measured results.
**Learned Autonomy Skills Development**
Formulate robot-learning problems including observations, actions, task goals, rewards, and policy structure. Implement and evaluate reinforcement learning, behavior cloning, or imitation-learning approaches; select and combine methods based on evidence. Build skills that handle variation, recover from failures, and integrate with existing planning and control systems.
**Data & Training Infrastructure**
Work with operators and robotics engineers to collect demonstrations, machine trajectories, interventions, and failure cases. Develop tools for synchronization, dataset curation, quality checks, versioning, and reproducible training. Identify data gaps and design targeted collection or simulation experiments to improve coverage and robustness.
**Simulation & Deployment**
Build and improve simulation-based training and testing; characterize sim-to-real differences. Address deployment constraints such as sensor noise, latency, action limits, inference performance, and changing machine dynamics. Integrate learned policies with perception, state estimation, controls, and runtime supervision, including fallbacks and mode transitions.
**Evaluation & Reliability**
Compare learned policies with appropriate baselines using held-out tasks and environments. Measure task success, intervention rate, cycle time, and constraint violations. Diagnose failures from logs and experiments; build regression tests. Validate changes through simulation and controlled hardware trials before field deployment.
**Cross-Team Leadership**
Lead cross-team execution, resolve technical blockers, mentor engineers, and personally implement critical system components. Influence technical decisions across robotics, controls, perception, software, and field teams. Raise engineering standards through design reviews, code reviews, and mentorship. Remain an active builder: writing code, training policies, debugging failures, and testing on hardware.
**Requirements**
Demonstrated technical ownership at Staff level or above of substantial robotics or robot-learning efforts, including setting direction, making architecture decisions, coordinating across teams, and delivering working systems. Track record of personally implementing, training, deploying, and evaluating learned policies on physical robots or vehicles. Depth in reinforcement learning, imitation learning, or behavior cloning is required; experience across RL and BC is welcome. Recent hands-on contributions to production or deployment-quality software, learning experiments, and hardware debugging, alongside technical leadership. Experience mentoring engineers and improving technical decision quality through design and code review. Strong understanding of machine learning, optimization, and sequential decision-making, including 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 sufficient to reason about 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 related field, or equivalent practical experience.
**Preferred Qualifications**
Sustained field or customer deployment of learned policies, including performance monitoring and regression prevention. Experience with demonstration collection, teleoperation, dataset design, offline RL, policy fine-tuning, or iterative data collection. Experience with simulation-to-reality transfer, system identification, domain randomization, or learning for contact-rich tasks. Experience with visuomotor policies, diffusion policies, action-sequence models, or hierarchical and multitask skill learning. C++ and experience integrating learned models into a robot software stack with real-time constraints. Experience applying robot learning to off-road machines, humanoids, manipulation, locomotion, or systems operating under variable loads and contact conditions. Hydraulic or electrohydraulic systems experience is a plus.
