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Machine Learning Engineer

Джерело:
djinni.co
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Що робити

  • Build & improve CV models – object detection, action recognition, semantic segmentation, tracking, and event classification for multi-camera sports footage.
  • Optimize for the edge – convert and quantize models (TFLite/ONNX/RT-Core) so they run in real time on Raspberry Pi 5 and ARM-based phones.
  • Own the ML pipeline – data collection, labeling guidelines, experiment tracking (Weights & Biases / MLflow), automated training, and CI/CD to Kubernetes.
  • Collaborate cross-functionally – work with backend, video-encoding, and mobile teams to integrate inference results into our NestJS APIs and Flutter/Angular clients.
  • Raise the bar – research state-of-the-art techniques, run ablation studies, author technical specs, and mentor future student interns.

Що очікуємо

  • Strong Python (3 yrs+) and deep-learning expertise with TensorFlow and/or PyTorch.
  • Solid background in computer vision: convolutional nets, attention mechanisms, spatio-temporal models (e.g., I3D, SlowFast).
  • Experience deploying models to mobile or edge hardware (TFLite, Core ML, TensorRT, or similar).
  • Familiarity with Docker & Kubernetes workflows for scalable training/inference.
  • Proven ability to turn research into production code: at least one project or product in the wild (GitHub, app store, or academic publication).

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