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FieldAIPosted 1 week ago

Principal Robotics Engineer

On-siteIrvine, California, United States

Full TimeSenior LevelSmall

Job Summary

Architect production-grade autonomy systems spanning perception, localization, planning, and control for legged and wheeled robots operating in unstructured global environments. Define high-stakes design decisions, stable API contracts, and quality standards across the end-to-end stack while driving root-cause analysis for fleet-wide reliability issues. Lead technical direction and mentor senior engineers to translate cutting-edge research into robust, field-ready capabilities without formal management authority. Establish observability practices and resolve complex bottlenecks in real-time systems to ensure scalable performance. Partner with research teams to mature learning-based methods from prototypes to deployed solutions.

Required Qualifications

  • Master's or PhD in Robotics, Computer Science, Electrical Engineering, or a related field—or equivalent industry experience
  • 10+ years of experience building production robotics or autonomy software, with a track record of Principal- or Staff-level technical impact
  • Deep expertise in modern C++ and Python, with strong command of ROS/ROS2, Linux, and Git
  • Demonstrated experience architecting large-scale autonomy systems across two or more of: perception, SLAM/localization, planning, and control
  • Strong foundation in real-time systems, concurrency, and performance optimization
  • Proven ability to lead complex technical efforts across multiple teams and to influence direction without formal management authority
  • Excellent written and verbal communication; able to make sound architectural trade-offs and articulate them to engineers and stakeholders alike

Desired Qualifications

  • PhD with a strong publication record in robotics, perception, or machine learning
  • Hands-on experience with both legged and wheeled robot platforms
  • Experience with learning-based methods for navigation, traversability, or planning, including vision foundation models or reinforcement learning
  • Experience with simulation environments such as Isaac Sim, Gazebo, or MuJoCo
  • Track record of deploying and maintaining robot fleets in harsh or unstructured real-world environments
  • Familiarity with fleet observability and performance tooling (e.g., Prometheus, Grafana)

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