Senior Computer Vision Engineer

🔥 36 minutes ago

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Panoptyc

WebsiteLinkedInAll Job Openings

1 - 10 employees

🤖 Artificial Intelligence

🔐 Security

🔧 Hardware

Artificial Intelligence • Security • Hardware

Panoptyc is a company dedicated to combating theft in micro markets using advanced surveillance technology. They offer a combined software and hardware solution that leverages artificial intelligence to recognize suspicious behavior and alert operators, significantly reducing shrinkage amounts and saving operators time and money. With state-of-the-art cameras and intelligent software that automatically highlights suspicious incidents, Panoptyc empowers teams to efficiently address and manage theft without the hassle of reviewing endless footage. Their system makes footage accessible even when faced with network issues, offering a powerful solution for micro market theft detection.

📋 Description

• 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