Computer Vision Engineer

Location: Austin, TX or Los Angeles, CA, US; based in or willing to relocate to Austin or Los Angeles

Company

hirify.global develops baseball and softball training products, using Smart Park camera systems, virtual reality, and computer vision to provide athletes, coaches, and teams with training data and insights.

What you will do

• Design, train, deploy, and optimize models for object detection, multi-object tracking, pose estimation, and event recognition from sports video.
• Build end-to-end pipelines that convert raw game and training footage into structured data on ball flight, player movement, swing mechanics, and pitch mechanics.
• Create datasets, labeling workflows, and evaluation frameworks using ground truth to measure model accuracy.
• Monitor performance across thousands of cameras and varied lighting, field conditions, and camera placements; identify and resolve failure modes.
• Work with product, backend, mobile, data, hardware, and Live Ops teams to ship model outputs as customer-facing features.
• Partner with hardware engineers on camera selection, placement, calibration, and synchronization, and bring relevant research advances into production.

Requirements

• MS or PhD in Computer Science, Electrical Engineering, or a related field, focused on computer vision or machine learning.
• 4+ years of industry experience in computer vision, including shipping models in commercial products used by real customers.
• Strong Python and C++; deep experience with PyTorch or a similar framework and OpenCV.
• Hands-on experience with object detection, multi-object tracking, camera calibration, and multi-view geometry or 3D reconstruction.
• Experience deploying and optimizing production models, including technologies such as ONNX, TensorRT, or GPU optimization.
• Based in or willing to relocate to Austin or Los Angeles.
• Strong fundamentals in deep learning, tracking, and multi-view geometry, with the ability to reason from first principles when standard approaches are insufficient.
• Ability to validate accuracy against ground truth, investigate field underperformance, and own model reliability and production outcomes.

Nice to have

• Experience at a sports data or sports technology company, including ball tracking, player tracking, or broadcast analytics.
• Baseball or softball background as a player or deeply engaged fan.
• Edge inference on embedded hardware such as NVIDIA Jetson.
• Pose estimation or biomechanics experience.
• Publications in computer vision or machine learning.

Culture & Benefits

• Individual-contributor role reporting to the Computer Vision Lead.
• Success is measured by accuracy against ground truth, production shipping, reliability across the camera fleet, and inference latency and cost per game.
• Close collaboration with data and backend engineering, product, hardware, and Live Ops.
• Full-time position based in Austin or Los Angeles.