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We’re hiring a hands-on Computer Vision Engineer to build and improve sports video intelligence models—detection, tracking, pose, event understanding, and multi-view reasoning. You’ll spend most of your time on CV research + applied modeling (experiments, architectures, training, evaluation), and partner with data/platform teammates to ensure your work can ship reliably. This role is CV-first. A bend toward scalable pipelines / MLOps is a plus, not a requirement. Level (mid vs senior) depends on scope ownership and how independently you can drive results. Responsibilities CV Modeling & Experimentation Build and train CV models for sports video: player/ball detection, multi-object tracking, pose/keypoints, event/action recognition, identity association (re-ID). Own the experimentation loop: hypotheses → ablations → error analysis → measurable improvements. Design and maintain evaluation: task-appropriate metrics (e.g., MOT metrics, keypoint accuracy, event precision/recall), dataset slices, and failure taxonomy. Improve data efficiency: augmentations, sampling strategies, handling label noise, weak/self-supervision where helpful. Prototype and iterate on modern architectures (e.g., transformer-based detection/tracking, temporal models, multi-task setups). Research that Ships Collaborate on dataset + labeling design: formats, schemas, tooling, versioning. Help productionize models: packaging, batch/stream inference patterns, throughput/latency tradeoffs, robustness checks. Add lightweight quality gates: reproducibility, automated eval, regression detection Qualifications Must-have: Strong applied CV experience with hands-on model development (not just running existing repos). Solid PyTorch skills: training loops, debugging, data pipelines for vision workloads, DDP basics. Comfort with video CV fundamentals: occlusion, identity switches, temporal consistency, calibration, domain shift. Strong Python engineering and a bias toward measurable outcomes. Nice-to-have (Bonus): Sports video CV or adjacent domains (multi-agent tracking, pose, crowded scenes). Experience with video tooling (FFmpeg), efficient dataset formats (WebDataset/shards), or streaming/batching to GPUs. MLOps/production experience: model packaging, CI for training/eval, serving (Triton/TorchServe), monitoring. Benefits Competitive Salary and Bonus Plan Comprehensive health insurance plan Retirement savings plan (401k) with company match Remote working environment A flexible, unlimited time off policy Generous paid holiday schedule - 13 in total including Monday after the Super Bowl SumerSports is committed to fair and equitable compensation practices. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor. The total compensation package for this position may also include annual performance bonus, benefits and/or other applicable incentive compensation plans.

What you’ll do

Develop and train computer vision models for sports video analysis, including detection, tracking, and action recognition. Collaborate with cross-functional teams to productionize models and ensure scalable, reliable performance.

Requirements

Requires strong applied experience in computer vision and hands-on model development using PyTorch. Candidates must demonstrate proficiency in Python and a deep understanding of video CV fundamentals like temporal consistency and occlusion.

Benefits

• Competitive Salary • Bonus Plan • Comprehensive Health Insurance • Retirement Savings Plan • 401k With Company Match • Remote Working Environment • Flexible Unlimited Time Off Policy • Paid Holiday Schedule

Listed skills

• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Computer Vision
• PyTorch
• Model Development
• Video Intelligence
• Object Detection
• Multi-object Tracking
• Pose Estimation
• Action Recognition
• Python
• MLOps
• Data Pipelines
• Temporal Modeling
• Transformer Architectures
• FFmpeg
• GPU Optimization
• Model Serving
• Object Tracking
• Transformer (Machine Learning Model)
• Pipelines
• MLOps (Machine Learning Operations)
• Quality Gate
• Taxonomy
• Research
• Multi-Agent Systems
• Calibration
• Course Evaluations
• Debugging
• Error Analysis (Numerical Analysis)
• Experimentation
• Packaging And Labeling
• Scalability
• Python (Programming Language)
• Multitasking
• Sampling (Statistics)
• Tooling
• Software Versioning
• Network Switches
• Video Player
• PyTorch (Machine Learning Library)

Job areas

• Technology
• Software
• Data & Analytics
• Engineering
• Sports & Recreation
• Computer Vision Engineer
• Software Developers