Robotics Autonomy Engineer - Planning and Control

FieldAI’s Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California’s robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence. If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field.

Field AI is 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.

Field AI is building the future of autonomy—from rugged terrain to real-world deployment. We’re 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’ll design, implement, and deploy path planning, trajectory planning, obstacle avoidance, and motion control algorithms that enable our robots to move with precision, robustness, and efficiency across wheeled, legged, and humanoid platforms. You’ll be part of a deeply technical team advancing real-world robotic capabilities through cutting-edge research, simulation tools, and field validation. If enabling robots to navigate challenging, dynamic environments excites you, and you want to work where your code hits the ground (literally)—this is your role. This is Field AI.

Design, develop, and refine path planning and navigation algorithms for challenging real-world scenarios such as narrow passages, dynamic obstacles, and off-road or unstructured environments.

Develop optimization based trajectory planning that ensures smooth, reliable, and efficient navigation across wheeled, legged, and humanoid platforms.

Build real time obstacle avoidance and reactive planning layers that keep robots safe among people, machines, and changing terrain.

Develop and tune control algorithms for precise trajectory tracking and stable operation across different robotic systems.

Plan and track within the constraints set by our independent safety layer, and work with the safety team to keep nominal behavior well inside the safe envelope.

Develop learning based planning and navigation, from learned navigation policies to foundation model driven mobility.

Collaborate across autonomy layers for seamless coordination between perception, planning, and control.

Build and maintain testing pipelines from unit-level validation to full robot deployment, using simulation for evaluation, benchmarking, and regression validation.

Analyze real-world telemetry to diagnose field issues and deliver targeted improvements while maintaining general-case reliability.

Master’s degree or higher in Robotics, Computer Science, Mechanical/Electrical Engineering, or a related field (PhD a plus)

Strong understanding of motion planning, trajectory generation, and control systems.

Experience in classical planning and control, such as path planning, trajectory optimization, and model predictive control (MPC)

Experience implementing learning based navigation, such as learned navigation policies or vision language action models (VLA) for mobility

Experience developing algorithms for one or more robotic systems (wheeled, legged, wheeled-legged, humanoid)

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, and wheel encoders

Exposure to real-world deployment of autonomous systems

Background in optimization, control, or numerical methods for trajectory planning

Experience with hybrid architectures that combine classical planners with learned components

Experience deploying vision language models (VLM) or vision language action models for navigation on real robots

Experience deploying planning or navigation stacks with real time onboard inference (ONNX Runtime, NVIDIA TensorRT)

Contributions to open-source planning or control frameworks

Familiarity with safety-critical autonomy and industrial robotics use cases

laptop-ats-crawler v2