UGV Systems Engineer – Robotics Hardware

UGV Systems Engineer – Robotics Hardware

Base pay range: $70,000.00/yr – $/yr

About Field AI

Field AI is transforming how robots interact with the real world. We build risk‑aware, reliable, and field‑ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence.

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: sensing systems (LiDAR, camera, TOF, IMU, GPS), embedded compute (CPUs, GPUs, microcontrollers, ROS, sensor drivers), 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).

UGV Integration Position

The UGV integration position will focus on integrating full stack autonomy hardware (sensors, computers, electromechanical systems) with wheeled Unmanned Ground Vehicles. This role will coordinate a team of electrical, mechanical, and computing engineers while also working closely with autonomy teams.

What You Will Get To Do

• Collaboratively direct the architecture and implementation of the UGV sensing and computing hardware to enable autonomy
• Direct sensor and compute payload integration:
  LiDAR, depth/stereo cameras, IMU/GNSS, embedded compute platforms (NVIDIA/Intel), time synchronization, ROS/ROS2 middleware
• Direct electrical design and platform integration: vehicle power distribution, onboard DC/AC systems, CAN/Ethernet networks, wiring harnesses, E‑stop/fault handling, diagnostics
• Direct mechanical integration activities: platform mounting on a wheeled UGV, ruggedization including vibration/thermal/ingress protection, ingress/egress for maintainability
• Define and execute field‑test readiness: develop test plans (thermal, vibration, EMI/EMC, power, latency/data integrity), lead debugging workflows and field root‑cause analysis
• Collaborate with autonomy software: drive hardware/sensor requirements aligned with autonomy algorithms, lead trade‑offs across compute, power, sensing and mechanical constraints
• Manage vendors and procurement: select sensors, compute modules, harnesses, mechanical components; establish QA checks; drive production readiness and scaling
• Mentor 2‑3 engineers, run design reviews, manage project timeline, ensure hardware documentation (I/O maps, sensor maps, launch files, configuration management)
• Work within a security‑conscious environment; U.S. citizenship and experience with cleared programs preferred

What You Should Have

• Education & Experience
• B.S., M.S., or Ph.D. in Mechanical Engineering, Electrical Engineering, Robotics, Computer Engineering or related field
• Several years of experience (5+ years) working with field robotics platforms (UGVs, wheeled/off‑road vehicles, quadrupeds, etc.) – ideally with experience across sensors, compute, mechanical and electrical systems
• Proven experience leading hardware programs and engineers through to field deployment

Technical Skills & Attributes

• Embedded compute experience: NVIDIA Jetson, Intel NUC/ARM SBCs, Linux, ROS/ROS2, sensor driver integration, coding (C++/Python)
• Sensor integration:
  LiDAR, depth/stereo cameras, IMU/GNSS, supporting buses (USB, Ethernet, CAN, GMSL, SPI/I²C)
• Sensor synchronization: strong familiarity with multi‑sensor time synchronization (PTP/NTP/PPS), spatial calibration (TF trees, URDF)
• Electrical systems: power architecture, onboard vehicle power, harness design, safety interlocks, fault diagnostics, CAN/Ethernet
• Mechanical/packaging: mounting sensors/compute on mobile platforms, vibration/thermal/ingress isolation, ruggedization for outdoor/field use, CAD design, thermal/structural analysis
• Systems‑level design thinking: trade between compute/thermal/power/sensor/performance constraints
• Field deployment experience: real‑world testing in harsh environments (vibration, dust, extreme temperatures, EMI), root‑cause analysis, QA and production readiness
• Strong cross‑discipline collaboration and communication…