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.
About the RoleYou’ll be the technical force behind our 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, all while pushing the boundaries of what’s possible in retail computer vision.
What You’ll DoModel Development: Design, train, and iterate on custom object detection models specifically tuned for retail environments, inventory tracking, and product recognition
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 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
LLM Integration: Explore and implement multimodal approaches combining vision and language models for enhanced product understanding and classification
5+ 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
Edge deployment mastery - experience with TensorRT, ONNX Runtime, or similar frameworks for optimizing models for constrained devices
LLM and LLMOps experience - practical knowledge of large language models, fine-tuning, prompt engineering, and building reliable LLM-powered systems
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 with retail, inventory management, or similar product-focused CV applications
Background with PyTorch and modern training frameworks
Familiarity with synthetic data generation and data augmentation techniques
Knowledge of model versioning, experiment tracking (MLflow, Weights & Biases, etc.)
Publications or open-source contributions in computer vision
Experience with AWS solutions like EC2, ECS, Fargate, S3, Bedrock, Sagemaker etc..
While we value expertise over specific tools, you’ll likely work with: PyTorch, YOLO variants, TensorRT, ONNX, Docker, Kubernetes, and various MLOps tooling.
Location: Remote
Compensation: up to $60/hr USD (based on location)
