Computer Engineering

Computer Vision Engineer

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. 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.

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.

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.
• Experience with multi-camera setups, stereo vision, or 3D reconstruction.
• Knowledge of real-time optimization techniques, parallel processing, or embedded CV deployment.

PyTorch, TensorFlow) for CV applications.

Familiarity with OpenGL, CUDA, or GPU programming for acceleration.

Experience working on embedded systems or with real-time video processing pipelines.

Exposure to MATLAB or ROS is beneficial for prototyping and testing.

Familiarity with Docker and Docker Compose for containerized ML toolingdeployment on cloud VMs.

Experience with Azure DevOps pipelines for automating ML training, evaluation, and model registration workflows.

We offer a highly competitive salary, comprehensive benefits including: medical and dental, vision, disability and life insurance, 401K, PTO, paid holidays, gear discounts and the ability to add value to an exciting mission!

Our Postings are not intended for distribution to or use in any jurisdiction, country or territory where such distribution or use would violate local law or would subject us to any regulations in another jurisdiction, country or territory. Equal Opportunity Employer Protected Veteran/Disabled

Revelyst is a collective of makers that design and manufacture performance gear and precision technologies. Our category-defining brands leverage meticulous craftsmanship and cross-collaboration to pursue new innovations that redefine what is humanly possible in the outdoors. Portfolio brands include Foresight Sports, Bushnell Golf, Fox, Bell, Giro, CamelBak, Bushnell, Simms Fishing and more ( .

All applicants are considered for employment without regard to race, color, sex (including pregnancy and related conditions, sexual orientation, or transgender status), national origin, religion, age (40 and older), equal pay, disability or genetic information (including family medical history or genetic tests or services), and retaliation for filing a charge, reasonably opposing discrimination, or participating in a discrimination lawsuit, investigation, or proceeding, and any other characteristics protected by law. If you need a reasonable accommodation because of a disability for any part of the employment process, please send an e-mail to View email address on click.appcast.io.