Embedded Systems Engineer, Humanoid Robotics

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.

In this role you will develop computing systems for humanoid robots. This may span compute platform design (ARM, SoC, microcontrollers), firmware and BSP bring-up, and kernel-level Linux work. Work will focus on a robot payload that intakes LiDAR, camera, IMU, and tactile sensor information and then outputs joint manipulation and locomotion commands that let the robot stand, move, and use its hands. 

You will partner closely with the ML team building the robot's software brain, ensuring the compute platform can run their perception and manipulation models with the latency and throughput they need. The system is designed to operate across a diversity of humanoid robot platforms, so your work will generalize across different hardware rather than target a single robot. You will collaborate closely with the mechanical, electrical, and ML teams to build tightly integrated, safety-conscious solutions ready for deployment in the field. 

• Humanoid Robotics Experience: Experience developing embedded systems, firmware, or drivers for humanoid or other legged robot platforms.

• Manipulation & Robot Control Knowledge: Familiarity with joint manipulation, motor control, and how sensor data flows into robot commands such as standing or grasping.

• Kernel-Level Development: Experience with Linux kernel modules, device driver development, kernel-level debugging, PREEMPT_RT, and deterministic, low-jitter timing in production systems.

• Safety-Critical Systems: Experience implementing e-stop circuitry, safety monitoring, or other safety-critical embedded systems for robots operating near people. Experience with functional safety standards such as ISO 13849 or ISO 10218 for robots operating in human environments.

• ML Collaboration: Experience working directly with ML or perception teams to meet model latency, memory, and throughput requirements on embedded hardware.