Join a fast-moving team building real-time vision systems that power advanced tracking and simulation technology. In this role, you will design and implement computer vision solutions that track objects and motion using high-speed, multi-camera data in a hardware-integrated environment.
What You’ll Do
Develop real-time algorithms for object detection, tracking, pose estimation, and motion analysis
Process high-frame-rate, multi-camera data to generate accurate 3D trajectories and impact insights
Collaborate with hardware, firmware, and simulation teams to integrate vision pipelines into embedded and desktop systems
Optimize performance using multithreading, SIMD, and GPU acceleration
Apply camera calibration, stereo vision, and sensor fusion for precise spatial modeling
Prototype new concepts, evaluate sensors, and support field testing
Write clean, testable code with unit and integration testing
Document algorithms, workflows, and data pipelines
Support ML workflows including dataset versioning, experiment tracking, and deployment (Azure ML)
Maintain MLOps tools (e.g., CVAT, training pipelines, evaluation workflows)
Required Qualifications
Bachelor’s or Master’s in Computer Science, Computer Engineering, Electrical Engineering, or related field
3+ years of computer vision experience in real-time, product-focused environments
Strong Python skills with OpenCV or similar libraries
Solid understanding of camera geometry, calibration, and lens distortion correction
Experience with multi-camera systems, stereo vision, or 3D reconstruction
Knowledge of tracking techniques (optical flow, Kalman filters, background subtraction, deep learning)
Experience with real-time optimization, parallel processing, or embedded CV deployment
Preferred Qualifications
C++, PyTorch, or TensorFlow experience
GPU programming (CUDA/OpenGL)
Embedded systems or real-time video pipelines
MATLAB or ROS exposure
Azure ML (workspaces, compute, experiment tracking)
Docker and containerized ML workflows
Azure ML DevOps pipelines for automated training and deployment