Machine Learning Engineer, Computer Vision (NXJ-218)

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MidZdalnie·WarszawaFull-time

#464355·Dodano 2 dni temu·3

Źródło: Newxel

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Tech Stack / Keywords

Python PyTorchTensorRTONNX RuntimeDockerFFmpegAzureYOLOSAMCVAT

Podsumowanie oferty

Own and scale the video understanding pipeline for a platform that extracts metadata, key moments, player statistics, and scene events from sports video. Improve computer vision services and evaluate models for production using Python, PyTorch, TensorRT, and ONNX Runtime.

Wymagania

3–5+ years of experience shipping production ML systems, including 2+ years in computer vision, ideally working with video.
Strong Python and PyTorch skills, including hands-on experience profiling and optimising GPU inference using TensorRT and ONNX Runtime.
Hands-on experience in at least two of the following areas: object detection and segmentation (YOLO, DETR-family, SAM); OCR and scene-text recognition; or video action recognition and temporal localisation.
Experience building test sets, calculating evaluation metrics such as mAP, F1, and temporal IoU, and running rigorous model comparisons.
Ability to critically evaluate research papers and model cards, and reproduce published results.
Production deployment experience with Docker, queue-driven services, and cloud GPU infrastructure.
Experience with video processing at scale, including FFmpeg, video decoding pipelines, and frame sampling.
Strong written communication skills, with the ability to document technical trade-offs and make clear recommendations.

Nice to have:

Experience with Azure (Service Bus, Blob Storage) and message-driven architectures.
Experience with vision-language models and multimodal understanding across video, audio, and text.
Experience fine-tuning or adapting vision models to specific domains, particularly sports video.
Experience with self-supervised learning or synthetic data generation to reduce labelling costs.
Familiarity with annotation tools such as CVAT and Label Studio, and experience working with labelling teams.
Experience with shadow deployments and A/B testing.
Familiarity with commercial model licensing.

Obowiązki

Own and improve the vision services:

Own the video understanding pipeline, including object detection, multi-object tracking, player re-identification, jersey-number recognition, and on-screen text recognition (OCR).
Own event and action recognition, including temporal localisation of key moments, shot and scene-boundary detection, replay detection, and camera-view classification.
Optimise GPU inference for throughput and cost through batching, precision tuning, and compilation.
Monitor production quality and latency, and investigate failures and regressions.

Evaluate and test new models:

Build and maintain labelled sports benchmarks, split by sport, league, and content type.
Define and track key metrics, including mAP, F1, precision, recall, IoU, latency, and GPU cost.
Run systematic comparisons of new detection, tracking, segmentation, re-identification, and video foundation models; reproduce published results on the company’s data.
Write decision memos with recommendations, trade-offs, and licensing considerations.
Own regression suites and shadow or A/B testing pipelines, ensuring every model or configuration change is validated before deployment.
Track new papers, model releases, and leaderboards, and identify developments for further evaluation.

Newxel

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