Staff AI Engineer | Robot Autonomy

Staff AI Engineer | Robot AutonomyMunich | €90,000 - €130,000 + ESOP | Humanoid RoboticsTeaching a robot to move an object across a table is one problem.Teaching it to find that object in an unfamiliar home, navigate towards it, pick it up safely and bring it to someone is a very different one.I’m working with an early-stage humanoid robotics company in Munich that is developing assistive robots for elderly people and their families. The technology has already been tested in real homes, and the next challenge is moving from individual demonstrations towards reliable autonomous behaviour.They are now hiring a Staff AI Engineer to take ownership of the robot-learning stack across data collection, model training, evaluation and deployment onto physical hardware.The roleYou will teach a physical robot to complete useful manipulation tasks in real domestic environments.The robot can already perform static pick-and-place tasks and operate light switches. The next behaviours include locating requested objects within a room, navigating towards them, retrieving items from the floor, handing them to a person and helping with tasks such as opening a water bottle.These tasks are not exceptionally complex in a controlled laboratory. The difficulty is making them work reliably across different homes, objects, lighting conditions, layouts and users.You will own the complete learning loop rather than focusing on one isolated model or research problem.What you will be working onDesigning real-world demonstration and data-collection workflowsTraining manipulation policies using learning from demonstration and behavioural cloningApplying reinforcement learning where it provides a practical improvementAdapting existing foundation models rather than building a general-purpose model from scratchEvaluating policies on physical robots and understanding why they failDeploying models onto real robotic systemsImproving performance using data collected from deployed robotsBuilding repeatable training, evaluation and deployment workflowsWorking closely with the robot platform and data infrastructure engineersHelping define the long-term autonomy architecture as the robot fleet growsThe likely technical direction includes existing VLM and VLA systems, NVIDIA GR00T, Pi-style models, diffusion policies, world models and action-chunking approaches.The expected balance is approximately 60% engineering and deployment, with 40% research. This is a hands-on individual-contributor role rather than a research-management position.What they are looking forExperience taking a learning-based manipulation system from data collection through to deployment on a physical robot.End-to-end robot learning on real hardwareManipulation, rather than perception aloneLearning from demonstration, imitation learning or behavioural cloningPractical reinforcement-learning knowledgeStrong Python and modern deep-learning frameworksC++ and strong general software-engineering foundationsROS2Robot kinematics, dynamics and controlTraining, evaluating and deploying policies on physical robotsDebugging failures involving calibration, latency, contact, data quality and hardware variationTaking ownership of ambiguous technical problems without waiting for a complete specificationA strong publication record is useful, but it will not replace evidence that you have built and deployed a working system.Candidates whose experience is limited to simulation, object segmentation, grasp detection or isolated motion-planning research are unlikely to be close enough to the problem.Useful additional experienceVLA modelsWorld modelsDiffusion policies or diffusion transformersAction chunkingBimanual or mobile manipulationHumanoid or dexterous roboticsJetson deployment and inference optimisationCompliant actuation or force-sensitive manipulationContinual or distributed learning systemsOpen-source robot-learning projectsExperience collecting demonstrations through teleoperationThe type of background that could fitHumanoid roboticsMobile manipulationDexterous manipulationWarehouse pickingGeneral-purpose robot-learning platformsRobotics foundation-model teamsAcademic robot-learning groups with substantial hardware ownershipWhat’s in it for you?The product is intended to support elderly people who want to retain more independence within their own homes.This is not a humanoid programme searching for a future use case. The team has already worked with elderly users and seen genuine demand for the product.You would be joining early enough to define how the autonomy system is built, while working directly with the CTO in a small and highly technical team.There is no mature legacy architecture to inherit. The successful hire will help decide the models, tools, data loops and engineering standards that the company builds around.The work will move quickly from code and experiments to physical robots operating in real environments.Location and packageLocation: MunichWorking model: Predominantly onsite due to the need to work with physical hardware; flexibility to work remotely when the task allowsSalary: €90,000–€130,000, depending on experienceEquity: ESOP in addition to salaryLanguage: English (Professional Competency)Right to work: Candidates cannot receive sponsorship; EU citizens or permanent residency is requiredInterview process20-minute introductory call with the CTOTwo-hour technical discussion covering background questions, collaborative architecture of a real problem and a walkthrough of an existing coding projectFinal conversation with the CEOApply through the link or message me directly with a short explanation of the robot-learning system you have personally taken from data collection to real-hardware deployment.