Member of Technical Staff, Microsoft Robotics (Navigation)

Microsoft•Redmond, WA

3d•$102,100 - $219,200

About The Position

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. 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. This role is for a Member of Technical Staff, Microsoft Robotics (Navigation) at the software engineer II level, to design, develop, and deploy navigation and planning systems that enable robots to move safely, efficiently, and reliably through real-world environments. The engineer will work across the autonomy stack, including localization, mapping, path planning, motion planning, behavioral decision-making, and controls integration, translating high-level mission intent into dynamically feasible robot motion. The role sits at the intersection of classical robotics, modern AI systems, simulation, and field deployment, with responsibility for building navigation capabilities that operate across diverse robot embodiments, sensor configurations, and operating conditions. The engineer will help advance Microsoft’s robotics platform by developing robust planning and navigation software that enables robots, AI agents, and human operators to coordinate effectively in physical environments.

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.
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 Haves

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 developing navigation, motion planning, path planning, behavior planning, or control algorithms for autonomous mobile robots, manipulators, drones, autonomous vehicles, or other embodied robotic systems.
Familiarity with robotics middleware and autonomy frameworks such as ROS/ROS2, Nav2, MoveIt, Drake, Autoware, or equivalent robotics software stacks.
Experience with planning approaches such as A*, D*, RRT/RRT*, lattice planning, trajectory optimization, model-predictive control, or learning-informed planning.
Experience integrating navigation systems with localization, mapping, perception, simulation, and robot control interfaces in real or simulated robotic platforms.
Proficiency in C++ and Python with experience writing performant, maintainable software for real-time or near-real-time robotics systems.
Experience debugging autonomy behavior using robot logs, telemetry, visualization tools, simulation replay, and field test data.
Familiarity with navigation challenges in dynamic, human-populated, degraded, unstructured, or partially observable environments.
Experience with simulation-based validation, scenario generation, autonomy benchmarking, or regression testing for robotics navigation systems.
Familiarity with learning-based navigation, imitation learning for navigation policies, or foundation models applied to planning and decision-making.
Understanding of safety-aware autonomy design, including collision avoidance, operational design domains, failure recovery, fallback behaviors, and human-in-the-loop intervention workflows.

Responsibilities

Develop algorithms that translate mission objectives, task assignments, spatial constraints, and robot state into safe, dynamically feasible trajectories for mobile robots, manipulators, or multi-robot systems.
Integrate navigation and planning modules with localization, mapping, perception, control, simulation, and fleet operations systems to support end-to-end autonomy workflows.
Build and improve real-time planning systems that operate under practical constraints such as limited compute, noisy sensors, dynamic obstacles, intermittent connectivity, and changing environmental conditions.
Develop costmaps, traversability models, spatial constraints, route-planning logic, and safety-aware decision policies that enable robots to operate in human-populated and operationally complex environments.
Use simulation, log replay, field testing, and automated regression suites to evaluate navigation performance, identify failure modes, and improve robustness across diverse scenarios.
Collaborate with perception, spatial AI, simulation, platform, and field test teams to validate navigation behavior across real and simulated environments, ensuring consistent performance from lab development through deployment.
Analyze robot telemetry, navigation logs, planner traces, and field test results to debug autonomy issues, tune planning parameters, and drive measurable improvements in success rate, safety, latency, and mission efficiency.
Write production-quality software in Python, C++, or equivalent languages, following engineering best practices for testing, maintainability, observability, and operational readiness.
Stay current with advances in robotics navigation, motion planning, model-predictive control, multi-agent planning, foundation models for robotics, and embodied AI, evaluating new techniques for adoption into Microsoft’s robotics platform.