Senior Computer Vision Engineer
Posted 19hrs ago
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Job Description
Senior Computer Vision Engineer developing cutting-edge models for retail object recognition and edge deployment strategy. Focused on custom YOLO architectures and open-source vision-language models.
Responsibilities:
- Design, train, and iterate on custom object detection models specifically tuned for retail environments, inventory tracking, and product recognition
- Fine-tune and deploy open-source vision-language models (LLaVA, Qwen-VL, InternVL, PaliGemma, etc.) for product understanding, zero-shot classification, and scene reasoning; build vision-language-action pipelines that translate visual understanding into downstream decisions
- Take state-of-the-art models and make them blazingly fast for edge deployment through quantization, pruning, and architectural optimization
- Build robust data pipelines and annotation workflows to continuously improve model performance on diverse retail scenarios
- Stay ahead of the curve on CV and VLM research, prototype new architectures, and determine what's actually production-ready versus academic noise
- Mentor engineers, establish best practices for model development, and drive technical decisions around our CV infrastructure
Requirements:
- 4+ years of hands-on computer vision engineering, with a proven track record of shipping models to production
- Deep expertise with YOLO and YOLO-E architectures - you've trained them, tuned them, and know their quirks intimately
- Hands-on experience with open-source VLMs (LLaVA, Qwen-VL, InternVL, PaliGemma, or similar) - fine-tuning, evaluation, and production deployment
- Familiarity with VLA frameworks and applying vision-language-action models to real-world perception and decision tasks
- Edge deployment mastery - experience with TensorRT, ONNX Runtime, or similar frameworks for optimizing models for constrained devices, including quantized VLMs
- Strong software engineering fundamentals - clean code, version control, CI/CD for ML, and the ability to build maintainable systems
- Production ML experience - you understand the difference between a Jupyter notebook and a production-grade ML system.
Benefits:
- Health insurance
- Flexible work arrangements
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