Computer Vision Engineer

Introduction

As a Computer Vision Engineer, 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.

Responsibilities

• Develop and implement real-time computer vision algorithms for object detection, tracking, pose estimation, and motion analysis.
• Work with high-framerate, 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

Required Skills:

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or related field.
• At least 3 years of experience in computer vision, preferably in a real-time, product-focused environment.
• Strong proficiency in 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.

Preferred Skills:

• Experience in C++ (a plus) and 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 ML DevOps pipelines for automating ML training, evaluation, and model registration workflows.