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# Computer Vision Engineer

- **Company:** FieldCoachAI LLC
- **Location:** United States (Remote available)
- **Experience:** Experienced
- **Salary:** $12.0 - $36,000.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Computer Vision, Cloud Computing, Python (Programming Language), NumPy, Object Detection, OpenCV, Performance Tuning, Tensorflow, SciPy, Systems Integration, Video Editing, Pytorch, Retrieval-Augmented Generation, Deep Learning, Containerization, ONNX (Open Neural Network Exchange) Format, Production Code, Model Architecture, Front End Software Development, TensorRT, Video Analysis, Data Pipelines, Docker
- **Published:** October 18, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=4b34ba96924cd67a

## About the Role

* Industry Track Record: 2+ years of professional engineering experience deploying deep learning and computer vision models into scalable production environments.
 * Core CV Frameworks: Mastery of Python and deep learning frameworks such as PyTorch or TensorFlow, along with foundational libraries like OpenCV, SciPy, and NumPy. Indeed
 * Video Analytics Expertise: Strong hands-on experience with object tracking (e.g., DeepSORT, ByteTrack) and temporal action segmentation in video sequences.
 * Mathematical Foundation: Strong grasp of linear algebra, multi-view geometry, vector calculus, and statistical data analysis. OpenCV
 * Production & Deployment: Experience with model containerization (Docker), cloud infrastructure (AWS or GCP), and writing clean, maintainable, production-ready code.

Nice-to-Haves

 * Experience with YOLOv8 Object Detection, Roboflow.
 * Familiarity with edge deployment, custom GPU acceleration, or optimizing pipelines for web-app delivery.
 * An interest or background in sports analytics, biomechanics, or computer vision in general.

## Description

As a Computer Vision Engineer, you will engineer the core architecture that interprets athletic film. You will design, train, and deploy production-grade models capable of precise object detection, human pose estimation, multi-object tracking, and complex spatial-temporal action recognition. Your work will directly power our automated analysis engine, calculating pinpoint metrics like break angles, acceleration bursts, and separation distance., * Model Architecture & Training: Design, train, and optimize state-of-the-art deep learning architectures (e.g., YOLO, Roboflow, ViTs, CNNs) for object detection, multi-player tracking, and precise 3D human pose estimation.

 * Pipeline Development: Build scalable, end-to-end video processing pipelines capable of handling high-volume, multi-angle game and practice footage.
 * Biomechanical Metrics Extraction: Develop geometric and spatial algorithms to extract granular athletic metrics, including joint angles, route break angles, velocity, and release mechanics.
 * Performance Optimization: Optimize models for efficient, high-throughput inference (utilizing TensorRT, ONNX, or custom quantization) to ensure rapid video processing turnaround.
 * Data Pipeline Ownership: Define dataset requirements, manage data annotation strategy and pipelines, and implement robust data augmentation techniques to handle varying lighting, camera angles, and jersey types.
 * Cross-Functional Integration: Collaborate closely with the platform engineering team to integrate model outputs seamlessly into our frontend UI and RAG-driven analytical architectures.