Senior Computer Vision Engineer

Panoptyc

Senior Computer Vision Engineer developing models for retail object recognition at Panoptyc. Building cutting-edge solutions for inventory tracking and product recognition.

Posted 7/14/2026full-timeRemote • 🇵🇭 PhilippinesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying custom object detection models and vision-language models for retail applications, with a strong focus on edge deployment and production ML systems. Proven ability to mentor teams and establish best practices in computer vision engineering.

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 Model DesignVision-Language Model DeploymentQuantization and PruningData Pipeline DevelopmentModel Performance Optimization

Soft Skills

MentoringTechnical Decision-Making

Tools & Technologies

TensorRTONNX RuntimeCI/CD for MLVersion Control

Industry Keywords

Retail EnvironmentsInventory TrackingProduct RecognitionVision-Language-Action ModelsScene Reasoning

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 optimize them 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 production-ready
Mentor engineers, establish best practices for model development, and drive technical decisions around our CV infrastructure

Requirements

What you’ll need

3+ 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

Competitive salary
Flexible working hours
Professional development opportunities