Computer Vision Engineer

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)

Do you have experience in Sensor calibration?, Do you have a Master's degree?, * 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

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