panoptyc - Sr. Computer Vision Engineer

• 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. • 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. • 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.