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

MLOps/production experience: model packaging, CI for training/eval, serving (Triton/TorchServe), monitoring.
Competitive Salary and Bonus Plan
Comprehensive health insurance plan
Retirement savings plan (401k) with company match
Generous paid holiday schedule - 13 in total including Monday after the Super Bowl
Remote working environment
Generous paid holiday schedule - 13 in total including Monday after the Super Bowl

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