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. 
 
 
 
 
 Hardware Team: 
 
 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. 
 
   
 
 Embedded Compute Role: 
 
 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.
What You Will Get To Do
• 1. Compute System Design
• 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.
• 2. Compute System Implementation
• 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.
• 3. Compute System Production & Servicing
• 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.
What Will Set You Apart
• Scaling: Experience taking systems from prototype to large scale production.
• Field Environments : Experience developing systems for harsh field environments.
• Deployed Robotics: Experience working on robotics deployed in real world settings such as autonomous vehicles, drones, or ruggedized robots.
• Systems Level Robotics: Fluency across software, electrical, and mechanical systems.
• Autonomy Software: Knowledge of autonomy stacks used in robotics. As well as how compute performance impacts autonomy algorithms.