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Member of Technical Staff, Microsoft Robotics (Robot Learning)

United States, Redmond Employment contract 102100.00 - 202200.00 USD / Year · Job Posted May 30, 2026
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Job Description

Microsoft's Discovery and Quantum (MDQ) division develops and delivers advanced artificial intelligence (AI), cloud-enabled capabilities, and strategic technologies to help solve the world's major challenges. From accelerating scientific discovery with advanced AI tools, to pioneering breakthroughs in quantum computing, to advancing robotics and AI capabilities that drive real-world impact, joining MDQ means building the future, partnering with fast-moving innovators, and operating in a high-impact, mission-driven environment. At Microsoft Robotics within MDQ, we build and deploy technologies that enable people, robots, and AI agents to collaborate and achieve more. We are building Microsoft's platform for physical intelligence—an integrated robotics software and AI platform that brings together humans, robots, and agents through robotics AI models, innovative teaming solutions and experiences, physically grounded agentic AI workflows, trustworthy test and evaluation, and real-world customer-focused validation. Built on Microsoft's core platforms and delivered through and with a global ecosystem of partners and customers, this platform accelerates AI for the physical world and helps robotics solutions move from experimentation to reliable, scaled deployment. We are hiring a Member of Technical Staff, Microsoft Robotics (Robot Learning) at the software engineer II level to develop, train, evaluate, and deploy machine learning models that enable robots to perceive, reason about, and act in the physical world. This engineer will work across the full robot learning stack, from data pipeline construction and model architecture experimentation through training at scale on GPU clusters to real-world deployment and evaluation on physical robot platforms. The role focuses on vision-language-action (VLA) and similar models, leveraging the breadth of frontier robot learning techniques and technologies (via, e.g., imitation learning, reinforcement learning), and other approaches that translate AI capabilities into reliable, generalizable robot behaviors for manipulation, navigation, and human-robot collaboration tasks. Microsoft's mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Job Responsibility

  • 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

Requirements

  • 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 OR equivalent experience
  • Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include, but are not limited to the following specialized security screenings Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter.

Nice to have

  • 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
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