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.
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
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
Experience developing solutions deployed to the NVIDIA Jetson family of products
Experience with retail, inventory management, or similar product-focused CV applications
Background with PyTorch and modern training frameworks ( Transformers, LitGPT, Unsloth, etc. )
Experience running VLM inference efficiently (vLLM, llama.cpp, SGLang, or similar)
Familiarity with synthetic data generation and data augmentation techniques
Knowledge of model versioning and experiment tracking ( MLflow, Weights & Biases, etc. )
Publications or open-source contributions in computer vision or multimodal AI
Experience with AWS: EC2, ECS, Fargate, S3, Bedrock, SageMaker, etc.
While we value expertise over specific tools, you'll likely work with: PyTorch, YOLO variants, open-source VLMs, TensorRT, ONNX, vLLM, Docker, Kubernetes, and various MLOps tooling.
Location: Remote
Panoptyc is building the future of retail intelligence. If you're ready to tackle hard CV and multimodal problems at scale, we want to hear from you.
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