Computer Vision Engineer

• 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.

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.