Senior Research Engineer – Perception

The autonomy team’s perception workload is growing rapidly with new platform variants and operational requirements. Working alongside the autonomy lead, you will be a key individual contributor pushing our perception capabilities forward. This is a research-first role with a direct path to deployed systems. You will tackle edge-case problems—such as dynamic battlefield geometries and active camouflage—while operating under strict computational constraints.

Expected Outcomes

• Rapid Deployment: Ship one major technical contribution within your first 2 months.

• Direct Impact: Contribute to at least one fully deployed perception module on our autonomous hardware.

• Applied Research: Transition complex theoretical models (like thermal fusion and anomaly detection) into practical, deployable code.

Сore responsibilities

• Advanced Sensor Fusion: Design and implement sensor fusion algorithms integrating RGB, LiDAR, Radar, and Thermal/IR imaging.

• Adversarial & Dynamic Detection: Develop novel algorithms to detect personnel and equipment employing active camouflage, and instantly map complex, fast-changing 3D geometries to distinguish clear routes from blocked terrain.

• Algorithm Optimization: Train and optimize deep learning models and probabilistic algorithms to run efficiently on edge computing devices (NVIDIA Jetson, FPGA) without sacrificing mission-critical latency.

• System-Level Integration: Build, test, and iterate on robotic prototypes, ensuring perception modules integrate seamlessly into our ROS2 architecture.

Qualifications & Competencies

• Education: MS or PhD in Robotics, Computer Science, Mathematics, Physics, or a related field requiring deep theoretical math.

• Experience: 5+ years of industry experience in robotics R&D. Strong publications or an equivalent applied research track record.

• Technical Stack: Proficiency in Python and C++ within ROS2. Expertise with PyTorch or JAX. Experience optimizing neural networks for embedded hardware (TensorRT, CUDA).

• Domain Expertise: Mastery of computer vision (3D object detection, depth estimation, semantic segmentation) and multi-modal sensor fusion techniques (Kalman Filters, Particle Filters, Bayesian networks).

• Defense Context: Experience with military or dual-use technologies, modeling environmental noise, and handling sensor uncertainty in contested environments is preferred.