Core Responsibilities
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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.
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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.
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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.
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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
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Experience: 3–5 years of hands-on experience deploying computer vision models into real-world production environments.
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Frameworks: Deep expertise in PyTorch, OpenCV, and the Hugging Face ecosystem.
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Tracking & Detection: Proven experience with YOLO variants, combined with tracking algorithms like ByteTrack, DeepSORT, or StrongSORT.
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Quantization & Serving: Familiarity with Triton Inference Server, vLLM, TensorRT-LLM, and ONNX Runtime.
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Infrastructure: Comfortable working in Ubuntu environments, handling RTSP/RTMP video streams, and collaborating within Docker/containerized workflows.
Work Location: In person
