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Deep Learning Engineer

Penelope Moreau

Contract · Job · New_York

About the role

Fast-moving team. Hard problems. No meetings before noon. We're building a real-time sports analytics platform that uses computer vision to track player movements, predict game outcomes, and generate automated highlight reels. Our models process 60fps video feeds from 20+ simultaneous camera angles. We need a deep learning engineer on a 12-month contract to help us build our next-gen player tracking models. You'll work on multi-object tracking, pose estimation, and action recognition — all at inference speeds that keep up with live broadcasts. The infrastructure is in place. The data is labeled. What we need is someone who can push model accuracy on the hardest sequences: occlusions, camera transitions, and fast-moving plays. If that sounds like fun, let's talk.

Responsibilities

  • Design and train multi-object tracking models for live sports video
  • Optimize inference pipelines for real-time performance at 60fps
  • Build evaluation benchmarks for tracking accuracy on challenging sequences
  • Collaborate with the video engineering team on camera calibration and feed preprocessing
  • Deliver weekly progress reports with quantitative results

Requirements

  • 5+ years in deep learning with a focus on video or vision tasks
  • Strong PyTorch and/or TensorFlow experience
  • Experience with multi-object tracking or pose estimation
  • Familiarity with model optimization (TensorRT, ONNX)
  • Comfort working independently on a contract basis

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Job Type

Contract

Level

Senior

Language

English

Salary Range

$130k – $160k / year

AI Expertise

AI & Machine Learning Engineers

Skills

python pytorch tensorflow computer vision docker aws

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