Sr. Computer Vision Engineer
New
P
PanoptycRetail Intelligence
Philippines, Brazil, ArgentinaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 3+ years
- Required Skills
- DockerKubernetesPyTorchMLOpsComputer Vision
Requirements
- 3+ years of hands-on computer vision engineering, with a track record of shipping models to production.
- Deep expertise with YOLO and YOLO-E architectures (training, tuning, and troubleshooting).
- Hands-on experience with open-source VLMs (LLaVA, Qwen-VL, InternVL, PaliGemma, or similar) including 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, specifically optimizing models for constrained devices using TensorRT, ONNX Runtime, or similar.
- Strong software engineering fundamentals, including clean code, version control, and CI/CD for ML.
- Experience building maintainable, production-grade ML systems.
- Experience with NVIDIA Jetson family of products is preferred.
- Background with PyTorch and modern training frameworks (Transformers, LitGPT, Unsloth).
- Experience running VLM inference efficiently (vLLM, llama.cpp, SGLang).
- Experience with AWS cloud services (EC2, ECS, Fargate, S3, Bedrock, SageMaker).
Responsibilities
- 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.
- Optimize state-of-the-art models 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.
- Mentor engineers, establish best practices for model development, and drive technical decisions around CV infrastructure.
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