As a
Computer Vision & Autonomy Engineer, you will be joining the team responsible for the design, development, and implementation of high-speed perception and autonomy stacks capable of identifying and tracking highly dynamic objects. You will solve the unique challenges of
high-dynamic sensing, where relative velocities are extreme and the margin for error is zero.
Key Responsibilities- Perception Pipeline Development: Develop robust real-time Deep Learning and Classical CV algorithms for classification, and tracking (e.g., YOLO, Transformer-based architectures) of highly dynamic objects.
- High-Speed State Estimation: Implement Visual-Inertial Odometry (VIO) and filtering techniques to estimate target 3D trajectories and "Time-to-Go" under high-G maneuvers.
- GPS denied perception stack: Create "GPS-denied" navigation solutions and anti-jamming vision pipelines that maintain autonomy when external signals are compromised.
- Guidance Logic: Design "Vision-Based Pursuit" laws and Proportional Navigation (PN) enhancements that translate visual target states into actionable steering commands.
- Real-time Deployment: Optimize algorithms for ultra-low latency execution on low-power devices, ensuring the "sensor-to-actuator" delay is minimized.
- Deterministic Benchmarking: 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.
- Dynamic 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.
