Computer Vision Engineer

Revelyst Outdoor•San Diego, CA

2d•$112,000 - $130,000•Onsite

About The Position

As a Computer Vision Engineer at Foresight Sports, you’ll design and implement vision-based systems that enable real-time tracking of golf balls, club heads, and player motion using high-speed camera data. Your work will directly power the core functionality of our flagship launch monitors and simulation products. This role blends algorithm development, machine vision, and performance optimization in a fast-paced, hardware-integrated environment. This position reports to the Firmware Engineer Manager and is based in our San Diego headquarters. As a Computer Vision Engineer you will have an opportunity to: Develop and implement real-time computer vision algorithms for object detection, tracking, pose estimation, and motion analysis. Work with high-frame-rate, multi-camera systems to extract 3D trajectory and impact data for golf ball and club head tracking. Collaborate with hardware, firmware, and simulation teams to integrate vision pipelines with embedded and desktop systems. Optimize code performance for real-time constraints using SIMD, GPU, or multithreading techniques. Apply image calibration, stereo vision, and sensor fusion techniques for accurate spatial modeling. Prototype and test new vision concepts, evaluate image sensor performance, and contribute to field trials. Write clean, modular, and testable code with unit and integration tests. Maintain detailed documentation of vision algorithms, workflows, and data pipelines. Support ML workflows including dataset versioning, experiment tracking, and model deployment on Azure ML infrastructure. Maintain and extend MLOps tooling — annotation pipelines (CVAT), training jobs, and model evaluation workflows.

Requirements

Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or related field.
3–5+ years of experience in computer vision, preferably in a real-time, product-focused environment.
Strong proficiency in C++ and Python, with experience using OpenCV or similar vision libraries.
Solid understanding of camera geometry, calibration (intrinsic/extrinsic), and lens distortion correction.
Experience with multi-camera setups, stereo vision, or 3D reconstruction.
Familiarity with object detection/tracking techniques (e.g., optical flow, Kalman filters, background subtraction, deep learning-based methods).
Knowledge of real-time optimization techniques, parallel processing, or embedded CV deployment.

Nice To Haves

Experience with machine learning frameworks (e.g., PyTorch, TensorFlow) for CV applications.
Familiarity with OpenGL, CUDA, or GPU programming for acceleration.
Knowledge of golf dynamics or sports motion tracking is a plus.
Experience working on embedded systems or with real-time video processing pipelines.
Exposure to MATLAB or ROS is beneficial for prototyping and testing.
Hands-on experience with Azure Machine Learning (workspaces, compute clusters, datastores, and experiment tracking).
Familiarity with Docker and Docker Compose for containerized ML tooling deployment on cloud VMs.
Experience with Azure DevOps pipelines for automating ML training, evaluation, and model registration workflows.

Responsibilities

Develop and implement real-time computer vision algorithms for object detection, tracking, pose estimation, and motion analysis.
Work with high-frame-rate, multi-camera systems to extract 3D trajectory and impact data for golf ball and club head tracking.
Collaborate with hardware, firmware, and simulation teams to integrate vision pipelines with embedded and desktop systems.
Optimize code performance for real-time constraints using SIMD, GPU, or multithreading techniques.
Apply image calibration, stereo vision, and sensor fusion techniques for accurate spatial modeling.
Prototype and test new vision concepts, evaluate image sensor performance, and contribute to field trials.
Write clean, modular, and testable code with unit and integration tests.
Maintain detailed documentation of vision algorithms, workflows, and data pipelines.
Support ML workflows including dataset versioning, experiment tracking, and model deployment on Azure ML infrastructure.
Maintain and extend MLOps tooling — annotation pipelines (CVAT), training jobs, and model evaluation workflows.