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
Technical Stack
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
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