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