Aimed at senior robotics engineers with PhD or equivalent experience who develop motion planning algorithms for real-world deployment.
Derived from job-description analysis by Serendipath's career intelligence engine.
Original posting from Field Ai via Lever
Who are We?
Field AIis transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications.
Learn more at https://fieldai.com.
About the Job
Field AI is building the future of autonomy, from rugged terrain to real-world deployment. We are on a mission to develop intelligent, adaptable robotic systems that operate beyond simulation and thrive in unpredictable environments.
As our Robotics Autonomy Engineer – Planning and Control, you will design, implement, and deploy advanced motion planning and control algorithms that enable our robots to move with precision, robustness, and efficiency across diverse environments. You will work on navigation, trajectory generation, and motion control for robotic platforms ranging from wheeled and legged systems to complex humanoid architectures. If enabling robots to navigate challenging, dynamic environments excites you, and you want to work where your code works in the physical world - this is your role. This is Field AI.
What You’ll Get To Do
- Develop Robust Motion Planning Algorithms
Design, develop, and refine motion and navigation planning algorithms for challenging real-world scenarios such as narrow passages, dynamic obstacles, and complex environments.
Design optimization-driven approaches for path and trajectory generation that ensure smooth, reliable, and efficient robot navigation across modalities.
Ensure scalability, reusability, and adaptability of planning approaches across diverse deployment contexts.
- Advance Control and Planning Integration
Develop and tune control algorithms that ensure precise trajectory tracking and stable operation across different robotic systems.
Collaborate across autonomy layers to ensure seamless coordination between perception, planning, and control for robust real-world performance.
- Validate and Test Across the Stack
Build and maintain testing pipelines from unit-level validation to full robot deployment.
Utilize simulation and testing environments for algorithm evaluation, benchmarking, and regression validation.
Analyze real-world telemetry to diagnose issues, identify improvements, and enhance algorithm robustness.
- Diagnose and Improve Field Performance
Investigate and resolve issues arising from field deployments through structured data analysis and debugging.
Deliver targeted improvements that address specific challenges while maintaining general-case reliability and performance.
What You Have
PhD degree in Robotics, Computer Science, Electrical Engineering, or a related field with 2+ years of industry or applied research experience or MS degree in a related field with 4+ years of relevant experience, or BS degree in a related field with 8+ years of relevant experience.
Strong understanding of motion planning, trajectory generation, and control systems.
Experience developing algorithms for one or more robotic systems (wheeled, legged, wheeled-legged, humanoid).
Familiarity with motion planning libraries like OMPL, MoveIt, Nav2 stack etc
Solid programming skills in C++ and Python on Linux-based systems.
Familiarity with robotics middleware such as ROS/ROS 2.
Experience with robot sensors including LiDARs, stereo/depth cameras, IMUs, GPS, wheel encoders.
The Extras That Set You Apart
Exposure to real-world deployment of autonomous systems.
Background in optimization, control, or numerical methods for trajectory planning.
Familiarity with learning-based or hybrid planning approaches.
Contributions to open-source planning or control frameworks.
Familiarity with safety-critical autonomy and industrial robotics use cases.
Source: Field Ai careers (Lever)