Hiring nowRobot learning and manipulation

Robot Learning Engineer

Also called Robotics ML Engineer · Embodied AI Researcher

A Robot Learning Engineer trains policies that let robots complete defined physical tasks reliably. The work turns a promising demo into behaviour a company can test, trust and put in front of customers.

What they do

The day starts with a task definition: what the robot must do, and what counts as failure. From there the engineer collects demonstrations or builds simulated environments, then trains policies using imitation learning, reinforcement learning or a mix. They run the policy in simulation, find where it breaks, and adjust data, rewards or observations. Then it moves to real hardware. They work with controls and mechanical colleagues on sensors, calibration, latency and safety limits. Every run is logged. A lot of time goes on working out why a policy that worked yesterday has stopped working today.

What a strong hire delivers

A strong hire gets one defined task running on real hardware within agreed force, speed and environment limits. They leave behind a trained policy, a repeatable training pipeline and clear test records from both simulation and the field. The team gains an honest picture of where the policy fails, how often, and what it would take to widen the conditions it handles.

How you can tell they are good

Ask them to walk through a policy they trained that ran on a physical robot. Strong people describe failures first. They can explain how simulation differed from reality and what they did about it. Ask how they decided a policy was ready. Listen for specific test conditions and limits. Be wary of candidates who only show simulation results or polished videos.

Why it is hard to fill

Very few people have trained policies that survived contact with real hardware. Titles vary widely, from robotics ML engineer to embodied AI researcher, and the same word can mean quite different work. Candidates tend to come from either a controls background or an ML background, and rarely feel equally at home in both. Demand is strong, and the best people are usually already settled in a lab or startup.

Where to find them

Look in robotics and control groups, mechanical and electrical engineering teams, and ML research groups that work with physical systems. Humanoid and robotics startups employ many of them, as do university and industry labs. Some come from autonomous systems or motion planning and have moved towards learned policies. Check for hands-on hardware time, not only papers or simulation work.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Robot Learning Engineer

  1. Tell me about a policy you took from simulation to a real robot. What broke first?

    A strong answer names concrete failures such as latency, friction, sensor noise or calibration drift, and the steps taken to fix them. Worry if everything transferred smoothly or the candidate cannot say what the gap was.

  2. When would you choose imitation learning over reinforcement learning for a manipulation task?

    Good answers weigh demonstration cost, reward design difficulty, safety during exploration and data quality. They often combine both. Worry about a fixed favourite method with no reasoning about the task.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are building robots that complete defined physical tasks reliably. We need a Robot Learning Engineer to train and validate the policies behind them, working closely with our controls, hardware and software colleagues. You will take work from simulation to real hardware and show clearly how well it performs.

What you will do

  • Train robot policies for defined manipulation tasks using imitation and reinforcement learning
  • Build and maintain simulation environments for training and testing
  • Transfer policies to real hardware and close the gap between simulation and reality
  • Collect and curate demonstration data
  • Test against force, speed and environment limits and record the results
  • Diagnose failures and report them plainly to the team

What you will bring

  • Trained policies that have run on physical robots, shown through demonstrated work
  • Hands-on experience with imitation learning or reinforcement learning
  • Working knowledge of robotics and control fundamentals
  • Experience with simulation and robot middleware
  • Comfort debugging hardware, sensors and timing issues
  • Clear written records of experiments and test results

Nice to have

  • Experience with motion-planning stacks
  • Computer vision for manipulation
  • Work on humanoid or dexterous hands

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Robotics and controlAdvancedPerceive, plan and act through physical systems
  • Reinforcement learningAdvancedOptimise policies from interaction and reward
  • Simulation and digital twinsWorkingBuild useful virtual representations of environments
  • Computer visionWorkingAnalyse or generate visual information

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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