Senior Computer Vision Engineer

Panoptyc

Senior Computer Vision Engineer developing cutting-edge models for retail object recognition and edge deployment strategy. Focused on custom YOLO architectures and open-source vision-language models.

Posted 7/23/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates deep expertise in computer vision and vision-language models, with a strong focus on deploying optimized models for retail environments. Proven ability to mentor teams and establish best practices in model development and deployment.

Highest-signal resume keywords

Computer Vision EngineeringYOLO Architecture ExpertiseOpen-Source VLM Fine-TuningEdge Deployment OptimizationProduction ML Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Object Detection ModelsModel Fine-TuningQuantizationPruningArchitectural OptimizationData Pipeline DevelopmentVersion ControlCI/CD for MLModel EvaluationScene Reasoning

Soft Skills

MentoringTechnical Decision-MakingBest Practices Establishment

Tools & Technologies

TensorRTONNX RuntimeLLaVAQwen-VLInternVLPaliGemma

Industry Keywords

Retail EnvironmentsInventory TrackingProduct RecognitionVision-Language-Action ModelsProduction-Grade ML Systems

About the role

Key responsibilities & impact

Design, train, and iterate on custom object detection models specifically tuned for retail environments, inventory tracking, and product recognition
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
Take state-of-the-art models and make them blazingly fast for edge deployment through quantization, pruning, and architectural optimization
Build robust data pipelines and annotation workflows to continuously improve model performance on diverse retail scenarios
Stay ahead of the curve on CV and VLM research, prototype new architectures, and determine what's actually production-ready versus academic noise
Mentor engineers, establish best practices for model development, and drive technical decisions around our CV infrastructure

Requirements

What you’ll need

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.

Benefits

Comp & perks

Health insurance
Flexible work arrangements