Embedded Systems Engineer – Robotics Hardware

The Hardware Team at Field AI develops perception and compute payloads that power autonomous robotics systems in complex real-world environments. Our work spans the full hardware stack designing and integrating sensing systems (LiDAR, camera, TOF, IMU, GPS), embedded compute (CPUs, GPUs, microcontrollers, Linux, ROS), electrical systems (power distribution, communication), and mechanical components (structures, thermal regulation, ingress protection). The team focuses on both development (research, design, prototyping, testing) and operations (production, testing, QA, debugging). We’re a small, fast-moving team, and we care deeply about improving: 1) core capabilities, 2) system reliability, 3) system scalability. As a growing team we are also building operational systems and procedures from the ground up.

 

As an Embedded Compute Engineer on the Hardware Team at Field AI, you will contribute to the architecture, configuration, and validation of the compute systems that serve as the backbone for our robotic platforms. Your work will span low-level firmware, Linux-based configuration, and system performance analysis across ARM and x86 SBC platforms. From firmware on microcontrollers to ROS data streams on SBCs, you’ll ensure the entire compute stack is optimized, reliable, and robust under field conditions. You will collaborate closely with the sensor, electrical, and autonomy teams to build tightly integrated solutions ready for deployment in challenging field environments. Additionally, while your focus will be on computing systems you will likely contribute across all hardware domains.

 

 

• Compute Architecture: Architect and configure embedded compute platforms (ARM/x86, SBCs) for robotic applications including evaluation, testing and selection.
• Firmware & Software: Set up and customize Linux environments (Ubuntu, Yocto, JetPack), middleware (ROS), and I/O interfaces.
• Systems Integration: Integrate compute with sensing and robotic systems. Analyze thermal, power, and bandwidth constraints to meet deployment and runtime requirements.

• Communications: Bring up sensors and peripherals using a range of protocols (USB, Ethernet, GMSL, I²C, SPI, CAN).
• Data Pipelines: Build and maintain drivers, ROS nodes, and data acquisition pipelines for new hardware components.
• Systems Configuration: Create configuration files, launch scripts, and firmware update workflows.
• Testing: Conduct system-level tests such as thermal profiling, latency measurement, and power draw analysis.
• Documentation & Budgets: Maintain flashing procedures, I/O maps, and debug kits. Manage compute and I/O budgets.

• Build: Work with vendors to procure compute hardware. Develop QA checks for incoming units. Support payload integration and scaling.
• Debug: Support root-cause analysis for boot, connectivity, and throughput issues.
• Diagnostics Monitoring: Implement watchdogs, health checks, and other evaluation tools.  Monitor compute system performance across CPU, GPU, memory, I/O, and networking.