About the Role
What You Will Get To Do
- Design and build the 3D scene/traversability representations our robots plan and act on — point cloud–based or learned implicit structures — optimized for real-time, on-robot use rather than offline reconstruction quality.
- Drive a long-term research agenda on learning-based approaches to building these representations — learned traversability, learned surface/reconstruction, geometry-aware embeddings — rather than only integrating existing classical pipelines.
- Stay close to the current literature on 3D scene representation for navigation — online navigation-mesh construction from streaming point clouds, learned elevation/traversability mapping, topologically-grounded navigation representations — and translate promising ideas into deployable systems.
- Design, implement, and maintain perception systems for autonomous robots operating in real-world environments.
- Develop localization and mapping capabilities that hold up in unstructured, off-road, and field conditions.
- Continuously evaluate and improve perception performance through testing, iteration, and field validation.
- Implement perception algorithms that fuse data from multiple sensors — LiDAR, cameras, RADAR, inertial sensors.
- Support integration of new sensing modalities and configurations as platforms evolve.
- Ensure perception software behaves consistently across simulation and real-world deployment.
- Take representations and algorithms from research prototype to production on physical robots.
- Debug issues discovered during on-robot testing and field operations.
- Collaborate with autonomy, controls, and platform teams to integrate cleanly into the full autonomy stack.
- Contribute to code quality, testing, and long-term maintainability.
- Build tools, metrics, and regression tests for representation quality and downstream navigation performance.
- Help scale representation and perception solutions across multiple robots, environments, and missions.
- Work with engineers, researchers, and field operators to define representation and perception requirements.
- Communicate technical tradeoffs clearly to both technical and non-technical stakeholders.
- Support field operations and customer demonstrations by keeping systems production-ready.
1. Own 3D Representation for Navigation (core focus)
2. Build and Maintain Perception Systems
3. Develop and Integrate Sensor-Based Perception
4. Deploy Perception Software on Real Robots
5. Improve System Robustness and Scalability
6. Collaborate Across Teams
What You Have
Bachelor’s or Master’s degree in Robotics, Electrical Engineering, Computer Engineering, Computer Science, Mechanical Engineering, or a related technical field.
3+ years of experience in verification, validation, systems test, or perception evaluation for robotics, autonomous systems, automotive, or similar domains.
Experience working with robotic sensors such as LiDAR, cameras, GPS, and IMUs.
Strong understanding of perception system behavior, sensor limitations, and common failure modes.
Experience developing test plans, validation procedures, performance metrics, and structured test reports.
Experience analyzing logs, datasets, and field results to debug issues and perform root-cause analysis.
Strong cross-functional communication skills and the ability to work effectively with development teams while representing an independent V&V function.
What Sets You Apart
Experience validating perception systems for autonomous vehicles, mobile robots, drones, industrial robots, or defense robotics platforms.
Familiarity with perception workflows such as detection, tracking, localization, mapping, or sensor fusion.
Experience with simulation, software-in-the-loop, hardware-in-the-loop, and replay-based validation.
Experience with sensor calibration, synchronization, time alignment, and sensor health monitoring.
Experience building automated regression tools or validation infrastructure.
Familiarity with annotated datasets, ground-truth generation, and scenario-based test design.
Knowledge of structured verification processes, requirements traceability, and safety-oriented development practices.
Skills Required
- Bachelor's or Master's degree in Robotics, Electrical Engineering, Computer Engineering, Computer Science, Mechanical Engineering, or related field.
- 3+ years of experience in verification, validation, systems test, or perception evaluation for robotics, autonomous systems, automotive, or similar domains.
- Experience working with robotic sensors such as LiDAR, cameras, GPS, and IMUs.
- Strong understanding of perception system behavior, sensor limitations, and common failure modes.
- Experience developing test plans, validation procedures, performance metrics, and structured test reports.
- Experience analyzing logs, datasets, and field results to debug issues and perform root-cause analysis.
- Strong cross-functional communication skills and ability to represent an independent V&V function.
- Experience validating perception systems for autonomous vehicles, mobile robots, drones, industrial robots, or defense platforms.
- Familiarity with perception workflows: detection, tracking, localization, mapping, sensor fusion.
- Experience with simulation, software-in-the-loop, hardware-in-the-loop, and replay-based validation.
- Experience with sensor calibration, synchronization, time alignment, and sensor health monitoring.
- Experience building automated regression tools or validation infrastructure.
- Familiarity with annotated datasets, ground-truth generation, and scenario-based test design.
- Knowledge of structured verification processes, requirements traceability, and safety-oriented development practices.
What We Do
FieldAI is pioneering the development of a field-proven, hardware agnostic brain technology that enables many different types of robots to operate autonomously in hazardous, offroad, and potentially harsh industrial settings – all without GPS, maps, or any pre-programmed routes.
.jpg)
