Senior Robotic Learning Engineer (m/f/d)

<div class="TranslationField-module__fieldItem___g4pRX"> <p><strong>The AI Teams at Agile Robots</strong> are looking for a <strong>Senior Robotic Learning Engineer (m/f/d)</strong>, who will architect the full pipeline that turns raw robot and human data into deployed foundation models, from collection through post-training to on-robot deployment.</p> </div> <div class="TranslationField-module__fieldItem___g4pRX"> <h3 class="TokenisedTypography-module__EzGiEkH0__v6-3-8 TranslationField-module__fieldItemName___Q0F_7">Your Responsibilities</h3> <ul> <li><strong>System Architecture:</strong> Own how collection, generation, augmentation, annotation, valuation, and evaluation fit together as one coherent data-to-model pipeline, and prioritize engineering investment where it most improves model performance.</li> <li><strong>Model Evaluation & Deployment:</strong> Integrate post-training and evaluation frameworks, in simulation and on real hardware, and own the path to reliable, automatic deployment on robotic platforms.</li> <li><strong>Data Valuation & Curation:</strong> Develop methods and metrics to assess data quality and value — diversity, coverage, redundancy, task relevance — and use them to guide what gets collected, kept, or discarded.</li> <li><strong>Data Generation:</strong> Build synthetic and simulation-based data generation pipelines, including real-to-sim-to-real transfer, procedural task and scene generation, and generative models, to scale training data beyond physical collection alone.</li> <li><strong>Data Collection:</strong> Design scalable systems for collecting both robot-generated data (teleoperation, autonomous rollouts, scripted policies) and human data (egocentric video, demonstrations) to train imitation learning and VLA models.</li> <li><strong>Cross-Team Collaboration:</strong> Partner with research scientists, robotics engineers, and infrastructure teams to translate model and research needs into production data and evaluation systems.</li> </ul> </div> <div class="TranslationField-module__fieldItem___g4pRX"> <h3 class="TokenisedTypography-module__EzGiEkH0__v6-3-8 TranslationField-module__fieldItemName___Q0F_7">Essential Skills</h3> <ul> <li><strong>Background:</strong> Master's degree or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related field, or an equivalent combination of formal training and professional experience.</li> <li><strong>Professional Experience:</strong> 5+ years in robotics, autonomous driving, machine learning, or data engineering, with demonstrated ownership of systems spanning multiple pipeline stages, not a single component.</li> <li><strong>Robot Learning:</strong> Hands-on experience with imitation learning, Vision-Language-Action models, robot foundation models, reinforcement learning, or other learning-based approaches to robotics.</li> <li><strong>Programming:</strong> Software engineering proficiency in Python sufficient to build production-grade, reliable systems rather than research prototypes; familiarity with C++ is a plus.</li> <li><strong>Systems-Level Thinking:</strong> Ability to reason about how a decision at one pipeline stage — what data is collected — affects outcomes at another, such as model performance or evaluation results, and to design across those boundaries.</li> </ul> </div> <div class="TranslationField-module__fieldItem___g4pRX"> <h3 class="TokenisedTypography-module__EzGiEkH0__v6-3-8 TranslationField-module__fieldItemName___Q0F_7">Beneficial Skills</h3> <ul> <li><strong>Simulation:</strong> Experience with simulation platforms such as Isaac Sim or MuJoCo for synthetic data generation and sim-to-real transfer.</li> <li><strong>Data Valuation Techniques:</strong> Familiarity with active learning, coreset selection, influence functions, or other methods for quantifying training data value.</li> <li><strong>Human Data Collection:</strong> Experience with egocentric data collection, AR/VR devices, hand tracking, or teleoperation frameworks for embodied AI.</li> <li><strong>Data Systems:</strong> Experience designing and operating large-scale data pipelines — collection, storage, processing, labeling — for machine learning training.</li> <li><strong>Deployment & M