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