Principal Robotics Learning Engineer

The posting, as AIM wrote it

ABOUT US

AIM builds autonomy for the real world—robots that move mountains. Our systems bring together software, hardware, and robotics to turn heavy equipment into intelligent machines that reshape how earthmoving is done.

Operating these machines requires skills that adapt to changing terrain, material properties, loads, and contact conditions. We’re looking for a Staff-level or more senior Robotics Learning Engineer to lead the technical development of data-driven autonomy skills, helping our machines learn and improve from demonstrations, simulation, and experience. This is a hands-on individual-contributor role on our technical career track, with level matched to experience and scope at Staff, Senior Staff, Principal, or above.

ABOUT YOU

You have led complex robot-learning efforts from an ambiguous problem through implementation and physical deployment. You can set the technical direction, break an effort into practical milestones, and bring other engineers along while remaining deeply involved in the code, experiments, and machine behavior.

You have built and deployed policies using reinforcement learning, imitation learning, or behavior cloning. You can explain the decisions you owned, the alternatives you tested, and the evidence that established whether the system worked. You bring the judgment to decide when to collect better data, improve the simulator, change the policy, or fix the surrounding robotics system.

You are comfortable with imperfect demonstrations, limited robot time, and simulation that does not perfectly match reality. You use experiments to distinguish a data problem from a modeling, learning, or integration problem, and choose methods based on measured performance.

You influence technical decisions across robotics, controls, perception, software, and field teams. You raise engineering standards through design reviews, code reviews, and mentorship, and take responsibility for delivering reliable machine behavior. You remain an active builder: writing code, training policies, debugging failures, and testing on hardware.

ABOUT US TOGETHER

We will build learned autonomy skills for heavy equipment, with potential applications such as digging, loading, grading, and interacting with variable materials. We will connect these skills to the broader autonomy system and use machine data to improve their reliability over time.

Our measure of progress will be repeatable task performance: successful execution, useful productivity, fewer interventions, and reliable operation across changing conditions.

WHAT YOU WILL OWN

LEAD THE TECHNICAL EFFORT

• Build and evaluate a proof of concept (POC) for a representative autonomy skill to establish feasibility and a measurable performance baseline.
• Use the POC to identify gaps in data, simulation, learning methods, machine integration, and evaluation, and prioritize what must improve.
• Develop and execute a phased technical roadmap based on the POC findings, from c

[…]
utions to production or deployment-quality software, learning experiments, and hardware debugging, alongside technical leadership.

• Experience mentoring engineers and improving the quality of technical decisions 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.
• A degree in Robotics, Computer Science, Electrical or Mechanical Engineering, or a related field, or equivalent practical experience.

PREFERRED QUALIFICATIONS

• Sustained field or customer deployment of learned policies, including performance monitoring, regression prevention, and improving reliability after launch.
• 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 other systems operating under variable loads and contact conditions, with the ability to transfer those methods to heavy equipment.
• Hydraulic or electrohydraulic systems experience is a plus, not a requirement.