Sensor Systems Engineer, Autonomous Vehicles (Federal)

About the role

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.

Who are We?

Field AIis 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.

About the Job

At FieldAI, we build autonomous robotic systems that operate in demanding, real-world environments where tight integration between hardware and software is critical. We’re looking for a Sensor Systems Engineer to support system and component level design, integration, and validation for sensing systems on wheeled robotic platforms across both federal and commercial programs.

Sensor Systems Role

As a Perception Systems Engineer on the Federal Team, you will contribute to the design, integration, calibration, and validation of the sensing and compute systems that underpin our autonomy stack. You will work with a diverse array of sensors-LiDARs, depth cameras, IMUs, GPS, and more-ensuring each one delivers accurate, time-synchronized, and spatially consistent data in a range of environments and operating conditions.

You will collaborate closely with the autonomy, compute, electrical, and mechanical teams to build tightly integrated solutions ready for deployment in challenging field environments. Additionally, while your focus will be on sensors you will likely contribute across all hardware domains.

What You Will Get To Do

• Sensor System Design

• Sensor Architecture: Work across hardware and software teams to define sensing requirements. Identity, test, and select sensors such as LiDARs, depth cameras, GPS, IMUs, ToF, and other devices.
• Systems Integration: Integrate sensors with electrical (USB, Ethernet, I2C, CAN, GMSL), mechanical (mounts, jigs, isolation), and software (configurations, drivers, ROS nodes) systems. Ensuring power, thermal, and protocol compatibility.
• Spatial Performance: Architect system to meet spatial sampling performance requirements including spatial coverage, range, resolution, and transform accuracy.
• Temporal Performance: Implement and validate synchronization strategies, triggering schemes, timestamp encoding, and end-to-end latency minimization.

• Sensor System Implementation

• Performance Characterization: Evaluate sensor performance (spatial resolution, temporal drift, EMI noise, sync errors etc.). Test under real-world environmental conditions (vibration, lighting, moisture, temperature).
• Calibration: Design and execute both intrinsic and extrinsic calibrations. Build alignment rigs and calibration boards as needed.
• Optimization: Tune sensor configurations (exposure, gain, filtering, frame rate, etc.) and processing pipeline (filtering etc.) for optimal performance
• Documentation: Create timing diagrams, sensor maps, coordinate transforms, and maintain all configuration files, launch scripts, driver references, and firmware states.

• Sensor System Production & Servicing

• Build: Work with vendors to procure sensors.
• Develop QA checks for incoming units.
• Support payload integration and manufacturing at scale.

• Debug: Conduct root cause analysis of sensor systems including issues across hardware and software boundaries.
• Monitor: Develop protocols for monitoring the sensing system health, data quality, and calibration status during field deployments.

What You Have

• Education: B.S., M.S. or Ph.D. in Robotics, Electrical Engineering, Engineering Physics, Physics, or a related field.
• Experience Level: We are recruiting across a wide range of experience levels from entry level engineers to senior and staff engineers.
• Hands-On Sensors Experience: Hands-on work with perception sensors such as LiDAR, depth cameras, IMUs, GPS.
• Systems Thinking: Cross-functional collaboration experience across electrical, mechanical, and software teams.
• Sensor Physics: Strong foundation in optics, signal processing, and electromagnetic wave propagation.
• Lab Skills: Hands-on testing, calibration, and debugging skills (oscilloscopes, logs, vibration/thermal setups)
• Communication Protocols: Experience with protocols such as USB, Ethernet, GMSL, CAN, I2C, and CSI.
• Computer & Software: Experience working with ROS/ROS2, sensor drivers, coordinate transforms (TF), and timing protocols (PTP, NTP, etc.).
• Timing & Triggers: Experience with timestamping, synchronization, triggering, and latency constraints.
• Calibration & Characterization: Experience designing and executing intrinsic, extrinsic, and temporal calibrations, along with sensor characterization under real-world environmental conditions.
• Simulation: Experience high-fidelity simulation environments (Gazebo, Isaac Sim, RViz) for sensor modeling.

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 sensor performance impacts autonomy algorithms.