Job Overview
Location
Philippines
Job Type
Full-time
Category
Android Developer
Date Posted
July 16, 2026
Full Job Description
đź“‹ Description
- • 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.
- • 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
🎯 Requirements
- • 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
- • 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.
Skills & Technologies
See exactly how your profile matches this role — strengths, skill gaps, and what to do about them.
About Panoptyc, Inc.
Panoptyc provides AI-driven theft-detection software for grocery and convenience retailers. Its cloud platform analyzes existing security camera feeds in real time, flagging suspicious behaviors such as skip-scanning, ticket switching, and product concealment. The system integrates with POS data to correlate transactions with video events, generating prioritized alerts for loss-prevention teams. The company serves multi-store chains in North America and Europe, aiming to reduce shrink without additional staffing or hardware.
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