Member of Technical Staff, Microsoft Robotics (field Robotics)

Microsoft Microsoft · Big Tech · Redmond, WA +1 · Hardware Engineering

This role focuses on planning, executing, and analyzing field tests of robotic systems in real-world environments to ensure reliability and performance. It bridges lab development and production readiness by deploying robots, designing test protocols, collecting data, diagnosing issues, and collaborating with engineering teams for continuous improvement. The role is part of Microsoft Robotics within the Discovery and Quantum division, building a platform for physical intelligence that integrates humans, robots, and AI agents.

What you'd actually do

  1. Plan, coordinate, and execute field test campaigns for robotic systems across diverse real-world environments, including customer sites, partner facilities, and first-party scenarios, ranging from indoor semi-structured settings to unstructured outdoor terrains, that replicate operational conditions.
  2. Develop and maintain structured field test protocols, acceptance criteria, data collection procedures, and reporting templates that ensure repeatable, measurable evaluation of robot hardware, software, and AI model performance.
  3. Deploy, configure, and operate robotic platforms in the field, including hardware setup, software provisioning, network configuration, sensor calibration, and integration with site-specific infrastructure.
  4. Collect, organize, and analyze field test data including sensor logs, telemetry streams, performance metrics, video recordings, and environmental measurements, producing clear and actionable test reports for engineering and program leadership.
  5. Collaborate with hardware engineers, software engineers, AI researchers, and program managers to communicate and integrate field findings, prioritize bug fixes and design improvements, and validate engineering changes through follow-up field testing.

Skills

Required

  • Bachelor's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field
  • 2+ years technical engineering experience

Nice to have

  • Doctorate in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field
  • Hands-on experience deploying, operating, or testing robotic systems, autonomous vehicles, drones, or complex electromechanical systems in field or operational environments.
  • Familiarity with common robotic systems implementations, including robotics middleware (ROS/ROS2), Linux-based systems, sensor suites (cameras, LiDAR, IMUs), and robot debugging and data visualization tools.
  • Experience with structured test planning, acceptance testing, or verification and validation (V&V) methodologies for hardware-software

What the JD emphasized

  • plan, execute, and analyze field tests
  • ensure reliable and scaled deployment
  • physically grounded agentic AI workflows
  • trustworthy test and evaluation
  • real-world customer-focused validation
  • AI models
  • field test campaigns
  • structured field test protocols
  • performance and telemetry data
  • diagnose field issues
  • actionable feedback
  • continuous improvement of hardware, software, and AI capabilities
  • robotic systems
  • AI behavior
  • engineering teams
  • field findings
  • engineering changes
  • field testing
  • field deployment
  • test procedures
  • field operations readiness
  • field deployment procedures
  • test triage
  • issue resolution
  • field campaigns
  • readiness programs
  • field operations training materials
  • robotic systems
  • autonomous vehicles
  • drones
  • complex electromechanical systems
  • field or operational environments
  • robotic systems implementations
  • robot debugging
  • data visualization tools
  • structured test planning
  • acceptance testing
  • verification and validation (V&V) methodologies
  • hardware-software

Other signals

  • physical intelligence platform
  • robotics software and AI platform
  • physically grounded agentic AI workflows
  • field tests of robotic systems
  • AI models and platform software and interfaces perform reliably