Senior Reality Capture & Sensor Payload Engineer, Robotics Hardware

About Us

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.

Reality Capture & Sensor Payload Engineer

In this role on the Hardware Team at Field AI, you will deliver the sensor modules that turn our robots into data-collection platforms for customers. This spans sensor selection, calibration, electro-mechanical integration, validation, and the data pipelines that carry sensor output from capture through processing. Examples of sensor modules include high-resolution LiDAR scanners, PTZ and thermal cameras, depth cameras, GNSS/RTK, and gas detection sensors. Candidates should be familiar with concepts and workflows such as laser scanning, photogrammetry, digital twins, BIM, and deviation and change analysis.

You will collaborate with the product team to select the right sensor for customers from hard industries such as construction and manufacturing. You will work closely with mechanical and electrical engineers to integrate each sensor into swappable modules on our robotic platform. You will own sensor performance such as calibration and time sync. As well as validate results through vibration and shock testing, point cloud evaluation, and other data quality checks. You may also lead the development of new custom reality capture sensors such as multicamera systems.

What You Will Get To Do

1. Sensor Selection & Cross-Team Coordination

• Sensor Selection & Evaluation: Evaluate and select sensors for reality capture and mapping, such as LiDAR, depth cameras, GNSS/RTK, and PTZ/thermal cameras, based on accuracy, range, and point density.

• Insights & Customer Alignment: Partner with Product and engage directly with customers across construction, manufacturing, and other industries, to reconcile ideal data requirements with physical constraints.

• Custom Systems: Propose custom sensor solutions when needed, assessing whether COTS (commercial off-the-shelf) choices are truly optimal or whether custom LiDAR or multi-camera systems perform better.

2. Module Hardware Design

• Calibration & Time Sync: Own multi-sensor calibration, including intrinsics, extrinsics, boresight alignment, and timing (PTP, PPS) across custom LiDAR and multi-camera systems.

• Mechanical & Electrical Integration: Work with the mechanical and electrical teams to design the integration unit that mounts sensors onto the robot in a plug-and-play way, within SWaP-C constraints.

• Ruggedization: Own mounting and enclosure design, including IP ratings and ruggedization, so modules hold up in harsh field conditions.

3. Data Processing & Validation

• Data Pipelines: Build and maintain data pipelines that turn sensor captures into registered, georeferenced point clouds and photogrammetric reconstructions for the Insights team.

• Testing & Validation: Test sensor performance, calibration accuracy, and point cloud repeatability under real-world conditions, vibration, shock, and lighting, before deployment.

• Production Quality: Support incoming quality checks and production consistency for ruggedized sensor modules as they scale across multiple robots.

4. Ownership & Leadership

• Systems-Level Optimization: Act as the systems-level owner who prevents siloed, locally-optimized solutions, finding the truly optimal fix across domains.

• Cross-Disciplinary Coordination: Serve as the connective point between Autonomy, Insights, Mechanical, and Electrical, keeping sensor payload decisions aligned across the organization.

• Team Leadership & Roadmap: Oversee and mentor junior mechanical and electrical engineers, and contribute to sensor and hardware roadmap and vendor strategy.

What You Have

• Education: B.S., M.S., or Ph.D. in Mechanical Engineering, Electrical Engineering, Robotics, or a related field.

• Experience Level: We're targeting 9+ years of experience for a senior to staff-level role, though we may consider more junior candidates with the right profile.

• Cross-Domain Engineering: Strong foundation across mechanical design, electrical design, and sensor hardware, with the breadth to work across all three.

• Data & Signal Processing Fluency: Working knowledge of point cloud and signal processing sufficient to evaluate software-based solutions to hardware problems.

• Systems-Level Optimization: Demonstrated ability to work across engineering domains and drive system-level, not just component-level, optimization.

• Sensor Knowledge: Deep familiarity with the sensor and geospatial-intelligence landscape, including LiDAR, cameras, thermal imaging, and gas detection.

• Mechatronics & Integration: Experience with electromechanical integration, mounting, enclosures, IP ratings, and connectors for ruggedized sensor payloads.

• Cross Functional Communication: Strong communicator able to arbitrate between teams that each see only their own piece of the solution space.

• Leadership: Experience managing or mentoring junior engineers on detailed design execution.

What Will Set You Apart

• Sensor Depth: Direct experience with sensors such as LiDAR, gas detection, thermal imaging, or PTZ camera systems.

• Field Robotics: Experience working with field robots subject to vibration, shock, or harsh environmental conditions such as construction or industrial sites.

• Mapping & Reality Capture: Familiarity with point cloud registration, photogrammetry and reconstruction, digital twin and BIM workflows, and deviation or change detection for environment mapping.

• Reality Capture Ecosystem: Familiarity with laser scanning hardware (FARO, Leica, Trimble) and reality-capture software platforms, applied to building or infrastructure inspection, construction verification, and asset management.

• Industry Exposure: Experience in construction, manufacturing, industrial, mining, or oil and gas environments, including hazardous-area (ATEX, IECEx, Class I Div 1/2) considerations.

• Research to Production: Experience taking sensor or perception work from research and algorithm development through to production deployment.