Panoptyc is seeking an exceptional Senior Computer Vision Engineer to architect and train cutting-edge models for retail object recognition and drive our edge deployment strategy.
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.