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Specialized edge-AI skillset plus metro location yields moderate applicant density and competition.
Edge perception and embedded deployment skills are highly domain-specific, limiting cross-industry transferability.
Requires advanced degree and strong, specific edge deployment and vision expertise, so filters are stringent.
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Design and deploy efficient perception models for real-time detection of road features and dynamic objects on resource-constrained edge devices.
Optimize AI pipelines and perform model conversion for low-latency inference on platforms like NVIDIA Jetson, Qualcomm Snapdragon, and ARM-based devices.
Collaborate with embedded systems and software teams to develop scalable training workflows and deliver production-ready edge AI solutions.
Advanced degree (MS/PhD) in Computer Science, AI, Robotics, or related field.
Strong experience in computer vision and deep learning model development.
Hands-on experience deploying AI models on edge or embedded hardware focused on real-time, low-latency inference.
Proficiency in Python and frameworks such as PyTorch or TensorFlow; experience with CUDA, TensorRT, or ONNX for model optimization and deployment.
Experienced in applied AI environments like automotive, robotics, or video/multi-camera perception systems with production deployments.
Skilled in modern vision architectures (YOLO, RT-DETR, MobileNet, EfficientNet, or lightweight transformers) and perception tasks including detection and segmentation.
Able to bridge the gap between research and production, owning end-to-end delivery of embedded AI perception systems with scalability and real-time performance.