Member of Technical Staff, Microsoft Robotics (Robot Learning)

Microsoft•Redmond, WA

6d•$102,100 - $219,200

About The Position

Microsoft’s Discovery and Quantum division develops advanced AI, cloud-enabled capabilities, and strategic technologies. Within Microsoft Robotics, the team builds technologies that enable people, robots, and AI agents to collaborate. Its physical intelligence platform combines robotics software, AI models, teaming solutions, physically grounded agentic AI workflows, trustworthy testing and evaluation, and customer-focused validation to help move robotics solutions from experimentation to reliable, scaled deployment. The Member of Technical Staff will develop, train, evaluate, and deploy machine learning models that enable robots to perceive, reason about, and act in the physical world. The role spans the robot learning stack, from building data pipelines and experimenting with model architectures to training at scale on GPU clusters and evaluating models on physical robot platforms. It focuses on vision-language-action models and related techniques, including imitation learning and reinforcement learning, to produce reliable and generalizable robot behaviors for manipulation, navigation, and human-robot collaboration.

Requirements

Bachelor’s degree in Computer Science or a related technical field and 2+ years of technical engineering experience coding in languages such as C, C++, C#, Java, JavaScript, or Python, or equivalent experience.
Ability to meet Microsoft, customer, and/or government security screening requirements, including passing the Microsoft Cloud Background Check upon hire or transfer and every two years thereafter.

Nice To Haves

Master’s degree in Computer Science or a related technical field and 3+ years of technical engineering experience coding in languages such as C, C++, C#, Java, JavaScript, or Python; or a bachelor’s degree in a related field and 5+ years of such experience; or equivalent experience.
Experience with end-to-end robot learning, including imitation learning, reinforcement learning, or training and deploying vision-language-action models on physical robots.
Proficiency in Python and deep experience with PyTorch, JAX, or TensorFlow for training and deploying deep learning models.
Experience with robot learning data pipelines, including teleoperation data collection, preprocessing, augmentation, and quality curation.
Hands-on experience deploying learned policies on physical robot platforms, debugging sim-to-real transfer challenges, and evaluating performance in real-world settings.
Familiarity with robotics middleware such as 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, including familiarity with leading open- and closed-source robotics models and emerging research technologies.
Experience with distributed training on GPU clusters and familiarity with Azure Machine Learning, Kubernetes, or equivalent infrastructure.

Responsibilities

Develop and train end-to-end robot learning models, including vision-language-action models, imitation learning policies, and reinforcement learning agents for manipulation, locomotion, and navigation.
Build, maintain, and optimize robot learning data pipelines, including teleoperation demonstration collection, preprocessing, augmentation, quality filtering, and dataset versioning.
Train machine learning and deep learning models on GPU clusters, implementing distributed training, hyperparameter optimization, curriculum learning, and training infrastructure automation.
Deploy trained models to physical robot platforms; evaluate them in real-world settings, debug sim-to-real transfer issues, and iterate based on deployment feedback.
Implement and maintain evaluation frameworks, 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 model development lifecycle from data collection through deployment and monitoring.
Write well-tested, maintainable, extensible, production-quality Python code, including use of NumPy, PyTorch, and JAX, in accordance with team coding standards and best practices.
Review code and technical designs, give feedback to develop other engineers’ skills, and promote coding patterns, security practices, and engineering excellence.
Follow state-of-the-art research in robot learning, robotics foundation models, and physical AI, and evaluate technologies and techniques for adoption and integration.
Contribute to knowledge sharing through technical documentation, brown bag sessions, blog posts, and mentoring team members.