Job Description

About the Role

You'll be joining our awesome team of hardware, full-stack and CV engineers developing our next generation computer vision capabilities, building and optimizing models that power real-world retail applications. This role demands someone who can move seamlessly from training custom YOLO architectures to deploying optimized models on edge devices - and from fine-tuning open-source VLMs to building VLA pipelines that reason about and act on what they see.

What You'll Do

• Model Development: Design, train, and iterate on custom object detection models specifically tuned for retail environments, inventory tracking, and product recognition

• VLM & VLA Integration: 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

• Edge Optimization: Take state-of-the-art models and make them blazingly fast for edge deployment through quantization, pruning, and architectural optimization

• Dataset Engineering: Build robust data pipelines and annotation workflows to continuously improve model performance on diverse retail scenarios

• Research & Innovation: Stay ahead of the curve on CV and VLM research, prototype new architectures, and determine what's actually production-ready versus academic noise

• Technical Leadership: Mentor engineers, establish best practices for model development, and drive technical decisions around our CV infrastructure

Required Experience

• 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

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