Member of Technical Staff, Microsoft Robotics (Robot Learning)

Develop and train end-to-end robot learning models, including vision-language-action (VLA) family of models, imitation learning policies, and reinforcement learning agents for manipulation, locomotion, and navigation tasks. Build, maintain, and optimize data pipelines for robot learning, including collection infrastructure for teleoperation demonstrations, data preprocessing, augmentation, quality filtering, and dataset versioning. Train machine learning and deep learning models on GPU computing clusters, implementing distributed training, hyperparameter optimization, curriculum learning, and training infrastructure automation. Deploy trained models to physical robot platforms, conducting real-world evaluation, debugging sim-to-real transfer issues, and iterating on model performance based on deployment feedback. Implement and maintain evaluation frameworks for robot learning models, including standardized task benchmarks, success rate tracking, generalization testing across objects and environments, and regression detection. Collaborate with robotics researchers, simulation engineers, and platform engineers to improve the end-to-end model development lifecycle, from data collection through deployment and monitoring. Write production-quality code in Python (including NumPy, PyTorch, JAX) that is well-tested, maintainable, and extensible, adhering to team coding standards and best practices. Review code and technical designs, providing feedback to develop other engineers' skills and drive adherence to coding patterns, security practices, and engineering excellence standards. Stay current with state-of-the-art research in robot learning, foundation models for robotics, and physical AI, evaluating new model technologies and techniques for adoption and integration into the platform. Contribute to internal knowledge sharing through technical documentation, brown bag sessions, blog posts, and mentoring of team members. Bachelor's Degree in Computer Science or related technical field AND 2+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python Master's Degree in Computer Science or related technical field AND 3+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 5+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Experience in end-to-end robot learning, including imitation learning, reinforcement learning, or vision-language-action model training and deployment on physical robots. Proficiency in Python with deep experience in PyTorch, JAX, or TensorFlow for training and deploying deep learning models. Experience with robot learning data pipelines, including teleoperation data collection, data preprocessing, augmentation, and quality curation for model training. Hands-on experience deploying learned policies on physical robot platforms, debugging sim-to-real transfer challenges, and evaluating model performance in real-world settings. Familiarity with robotics middleware (ROS/ROS2), robot control interfaces, and sensor processing for perception-action loops. Track record of following state-of-the-art research in robot learning, foundation models, and physical AI, e.g., familiarity with latest leading robotics models (open- and closed-source), and emerging technologies across academia and robotics research labs. Experience with distributed training on GPU clusters, including familiarity with Azure Machine Learning, Kubernetes, or equivalent infrastructure.