Computer Vision Engineer

WIN Reality, LLC Computer Vision Engineer Austin, TX · Los Angeles, CA · Full time Company website Apply for Computer Vision Engineer

WIN’s computer vision team turns video into sports insights. Our models extract data from game and training footage — captured by thousands of Smart Park camera systems at baseball and softball fields — and turn it into stats, metrics, and insights that help players, coaches, and teams get better.

About WIN Reality, LLC

WIN Reality is changing how baseball and softball players across the world train to get better. Our team is full of world class developers changing the VR landscape, former professional ballplayers dedicated to improving the way the game is played, and people passionate about creating a one of a kind experience. We’re always looking for talented people to join our team, and to continue pushing the boundaries of what’s possible.

Description

Department: Engineering

Reports To: Computer Vision Lead

Location: Austin, TX or Los Angeles, CA

About the Role

WIN’s computer vision team turns video into sports insights. Our models extract data from game and training footage — captured by thousands of Smart Park camera systems at baseball and softball fields — and turn it into stats, metrics, and insights that help players, coaches, and teams get better.

As a Computer Vision Engineer, you’ll build and ship the models and pipelines behind those insights: detecting and tracking the ball and players, measuring what happened on every pitch and swing, and doing it reliably across real-world fields, lighting, and camera angles.

This is a role for an engineer who has shipped computer vision in commercial products used by real customers — not just research or demos. Success is measured by the accuracy, reliability, and value of the data we deliver.

Key Responsibilities

Models & Pipelines

Design, train, and deploy models for object detection, multi-object tracking, pose estimation, and event recognition from sports video.

Build end-to-end pipelines that turn raw video into structured, reliable sports data — ball flight, player movement, and swing and pitch mechanics.

Optimize models for production: accuracy, latency, cost, and edge vs. cloud inference.

Data & Evaluation

Build datasets, labeling workflows, and evaluation frameworks that measure accuracy against ground truth.

Monitor model performance in the field across thousands of cameras, varied lighting, field conditions, and camera placements.

Find failure modes fast and close them with better data, better models, or better calibration.

Shipping Product

Work with product, backend, and mobile engineers to turn model outputs into features customers use every day.

Partner with hardware on camera selection, placement, calibration, and synchronization.

Stay current on research and bring the right advances into production.

Team & Structure

Individual contributor on WIN’s computer vision team, reporting to the Computer Vision Lead.

Close partners: data and backend engineering, product, hardware, and Live Ops.

Who You Are

You’ve shipped. Your models run in commercial products used by real customers, and you know what it takes to get them there and keep them working.

You go deep. You have strong fundamentals in deep learning, tracking, and multi-view geometry, and you can reason from first principles when off-the-shelf approaches fall short.

You care about accuracy you can prove. You measure against ground truth, and you don’t trust a metric you can’t validate.

You love sports. You know what spin rate and exit velocity mean, and you’re excited to build the data that athletes and coaches rely on. Experience at a sports data company is a big plus.

You’re pragmatic. You pick the simplest approach that hits the bar, and you ship it.

You own outcomes. When a model underperforms in the field, you dig in until it’s fixed.

How Success Is Measured

Primary KPI: Accuracy of computer vision–derived metrics against ground truth.

Shipping: Models and improvements deployed to production on plan.

Reliability: Consistent performance across the deployed camera fleet and real-world conditions.

Efficiency: Inference latency and cost per game processed.

Qualifications

Required

MS or PhD in Computer Science, Electrical Engineering, or a related field, with a focus on computer vision or machine learning.

4+ years of industry experience in computer vision, including shipping models in commercial products.

Strong Python and C++; deep experience with PyTorch (or similar) and OpenCV.

Hands-on experience with object detection, multi-object tracking, camera calibration, and multi-view geometry or 3D reconstruction.

Experience deploying and optimizing models in production (for example, ONNX, TensorRT, GPU optimization).

Based in, or willing to relocate to, Austin or Los Angeles.

Preferred

Experience at a sports data or sports technology company (ball tracking, player tracking, or broadcast analytics).

Background in baseball or softball, as a player or a deep fan of the game.

Edge inference on embedded hardware (for example, NVIDIA Jetson).

Pose estimation or biomechanics experience.

Publications in computer vision or machine learning.

Apply for Computer Vision Engineer