Senior AI Scientist (Vision Lead)

We are looking for a Sr. AI Scientist to own and scale our Vision AI Center of Excellence (COE). You arent joining a research lab to write theoretical papers; you are here to build production-grade, real-time spatial analytics that drive immediate revenue in heavy industries like ports, logistics, and manufacturing. You will take our working Ubuntu CPU-based Edge relay software (YOLO + RTSP/RTMP encoding) and turn it into an enterprise-grade live video analytics platform. You will work alongside our Platform team to deploy models directly into turnkey Edge NVR hardware boxes and air-gapped environments. Core Responsibilities Spatio-Temporal Pipeline Engineering: Advance our current YOLO pipeline from single-frame object detection to multi-object tracking (MOT) across space and time for vehicle tracking, worker safety compliance, and people analytics. VLM Fine-Tuning Quantization: Adapt, fine-tune, and optimize state-of-the-art open-weight Vision-Language Models (e.g., Qwen2.5-VL/Qwen3-VL, LLaVA, SAM) for highly localized, industry-specific tasks. Edge Optimization: Work closely with MLOps to compress models using quantization frameworks (AWQ, GPTQ) so complex tracking and safety logic can run efficiently on NVR boxes and CPU/NPU edge nodes. Synthetic Data Pipelines: Build automated data curation and synthetic data generation loops to handle poor lighting, rusted container codes, and unique industrial edge cases.

Required Technical Skillset Experience: 35 years of hands-on experience deploying computer vision models into real-world production environments. Frameworks: Deep expertise in PyTorch, OpenCV, and the Hugging Face ecosystem. Tracking Detection: Proven experience with YOLO variants, combined with tracking algorithms like ByteTrack, DeepSORT, or StrongSORT. Quantization Serving: Familiarity with Triton Inference Server, vLLM, TensorRT- LLM, and ONNX Runtime. Infrastructure: Comfortable working in Ubuntu environments, handling RTSP/RTMP video streams, and collaborating within Docker/containerized workflows. This job is provided by Shine.com