Qualifications :

• Minimum 3+ years of industry experience in developing and deploying computer vision systems for video analytics at scale.
• Proven track record of production deployments across large-scale camera networks ,including full lifecycle from prototyping to monitoring.
• Demonstrated expertise in building and optimizing Vision-Language Models (VLMs) for edge environments, with hands-on experience in architectures like unified embedding, cross-modality attention, or efficient variants (e.g., SmolVLM, LFM2-VL, MobileVLM).
• Deep understanding of performance bottlenecks in contemporary video analytics and VLM systems (e.g., GPU/CPU saturation, PCIe bandwidth contention, codec latency, drift due to domain shift, high token counts in multimodal processing, and privacy-preserving inference).
• Hands-on expertise in edge model optimization using TensorFlow Lite, ONNX Runtime, PyTorch Mobile, OpenVINO, TensorRT, or TVM, achieving ≥2–5x reductions in latency/memory while maintaining accuracy, including techniques for VLM compression like token pruning or multi-scale pooling.
• Strong proficiency in Python/C++, with extensive experience in PyTorch/TensorFlow, OpenCV, CUDA, and distributed training/inference frameworks.
• Solid foundation in modern CV architectures (Transformers, CNNs, hybrid models), real-time tracking algorithms (DeepSORT, ByteTrack, BoT-SORT), and VLM components (e.g., vision encoders like ViT, multimodal pre-training strategies).

What we offer:

• Competitive compensation package with equity.
• Comprehensive health benefits and flexible working arrangements.
• Access to cutting-edge hardware, cloud credits, and conference attendance support.
• Opportunity to shape the future of AI-powered physical security systems.