Computer Vision & Autonomy Engineer

Key Responsibilities

• Develop robust real-time Deep Learning and Classical CV algorithms for classification, and tracking (e.g., YOLO, Transformer-based architectures) of highly dynamic objects.
• Implement Visual-Inertial Odometry (VIO) and filtering techniques to estimate target 3D trajectories and "Time-to-Go" under high-G maneuvers.
• Create "GPS-denied" navigation solutions and anti-jamming vision pipelines that maintain autonomy when external signals are compromised.
• Design "Vision-Based Pursuit" laws and Proportional Navigation (PN) enhancements that translate visual target states into actionable steering commands.
• Optimize algorithms for ultra-low latency execution on low-power devices, ensuring the "sensor-to-actuator" delay is minimized.
• Profile and eliminate "long-tail" latency spikes in the autonomy stack to ensure a deterministic sensor-to-actuator response time.

Required Qualifications

• Education: Master’s or PhD in Robotics, Computer Science, or Aerospace Engineering with a focus on Computer Vision or Autonomous Systems.
• Dynamics Vision skills: Expert knowledge of object tracking (KCF, SORT, DeepSORT) and the geometry of moving camera platforms.
• Real-Time Software: Proficiency in C++20 and CUDA for high-throughput image processing, and Python for training ML models.
• Mathematics: Deep understanding of 3D geometry, Kalman Filtering (EKF/UKF), and the physics of relative motion.

Preferred Skills

• EO/IR camera: Experience working with Long-Wave Infrared (LWIR) or Mid-Wave Infrared (MWIR) sensors.
• Embedded Systems: Experience deploying models on NVIDIA Jetson Orin or FPGA-based vision processing.
• High-Fidelity Simulation: Proficiency in NVIDIA Isaac Sim, Unreal Engine 5, or Gazebo to generate synthetic data for rare "corner-case" scenarios.
• Control Integration: Understanding of how perception latency affects the stability of flight control loops.

Compensation Range

$80K - $120K