Computer Vision Engineer

Be an Early Applicant Remote Hiring Remotely in USA Senior level Remote Hiring Remotely in USA Senior level Design, train, and deploy production computer vision and vision-language models for retail product recognition. Optimize models for edge devices, build data pipelines and annotation workflows, fine-tune open-source VLMs, develop VLA pipelines, mentor engineers, and drive CV infrastructure and MLOps best practices. The summary above was generated by AI Computer Vision Engineer

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. What You’ll Do

• 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, Computer Vision * Machine Learning * Software Design, train, and deploy custom object-detection models for retail; fine-tune and integrate vision-language models; optimize models for edge devices; build dataset and annotation pipelines; prototype research ideas; and provide technical leadership and mentoring for CV infrastructure and production ML systems. Top Skills: BedrockDockerEc2EcsFargateInternvlKubernetesLitgptLlama.CppLlavaMlflowNvidia JetsonOnnxOnnx RuntimePaligemmaPyTorchQwen-VlS3SagemakerSglangTensorrtTransformersUnslothVllmWeights & BiasesYoloYolo-E NVIDIA

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• 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

Preferred Qualifications

• 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.