What are the responsibilities and job description for the Computer Vision Engineer position at Lynn Rodens?
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
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)
- 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
- 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