Cover is an AI security company developing concealed weapon detection systems. Cover's imaging technology scans students for concealed weapons in K‑12 schools in the United States. Our goal is to deter school shootings by identifying concealed weapons inside of bags and underneath clothing.We are headquartered in San Jose, CA with offices in Pasadena, CA and require 5 days/week in‑office work.In order to scale to the 130,000 K‑12 schools in the United States, we will need to detect weapons with fully autonomous AI models. We are looking for a deep learning engineer to take in sparse point clouds and output weapon models with low false positives. Your goal is to design weapon detection models with low latency and high accuracy to prevent school shootings.ResponsibilitiesResearch, design, implement, optimize and deploy deep learning models that advance the state of the art in autonomous weapons detection modelsOperate with a commercial mindset to ship working product that can detect weapons at K‑12 schools in the United StatesDevelop training pipeline including synthetic data generation and validation against real sensor dataTrain machine learning and deep learning models on a computing cluster to carry out visual recognition tasks, including weapon segmentation and detectionReview deep learning code and research papers, implement models and algorithms, tailor them to our specific use cases for school weapon detection, enhance internal metrics, and collaborate with downstream engineers to efficiently integrate neural networks into our scanning systemEnhance deep neural networks and their related preprocessing and postprocessing code to ensure efficient execution on an embedded deviceRequirementsExperience with PyTorch, or at least another major deep learning framework such as TensorFlow, MXNetDeep comprehension of the foundational principles of deep learning, including layer architecture, backpropagation, and other essential conceptsExperience with object detection and classification using point cloud data from radar or lidarStrong desire to help ship AI systems that can prevent school shootingsDue to technology export restrictions all applicants must be a US Person (citizen or legal permanent resident)US base salary range for this full‑time position is between $150,000 - $350,000 annually.The pay offered for this position may vary based on several individual factors, including job‑related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.#J-18808-Ljbffr
Cover is an AI security company developing concealed weapon detection systems. Cover's imaging technology scans students for concealed weapons in K‑12 schools in the United States. Our goal is to deter school shootings by identifying concealed weapons inside of bags and underneath clothing.We are headquartered in San Jose, CA with offices in Pasadena, CA and require 5 days/week in‑office work.In order to scale to the 130,000 K‑12 schools in the United States, we will need to detect weapons with fully autonomous AI models. We are looking for a deep learning engineer to take in sparse point clouds and output weapon models with low false positives. Your goal is to design weapon detection models with low latency and high accuracy to prevent school shootings.ResponsibilitiesResearch, design, implement, optimize and deploy deep learning models that advance the state of the art in autonomous weapons detection modelsOperate with a commercial mindset to ship working product that can detect weapons at K‑12 schools in the United StatesDevelop training pipeline including synthetic data generation and validation against real sensor dataTrain machine learning and deep learning models on a computing cluster to carry out visual recognition tasks, including weapon segmentation and detectionReview deep learning code and research papers, implement models and algorithms, tailor them to our specific use cases for school weapon detection, enhance internal metrics, and collaborate with downstream engineers to efficiently integrate neural networks into our scanning systemEnhance deep neural networks and their related preprocessing and postprocessing code to ensure efficient execution on an embedded deviceRequirementsExperience with PyTorch, or at least another major deep learning framework such as TensorFlow, MXNetDeep comprehension of the foundational principles of deep learning, including layer architecture, backpropagation, and other essential conceptsExperience with object detection and classification using point cloud data from radar or lidarStrong desire to help ship AI systems that can prevent school shootingsDue to technology export restrictions all applicants must be a US Person (citizen or legal permanent resident)US base salary range for this full‑time position is between $150,000 - $350,000 annually.The pay offered for this position may vary based on several individual factors, including job‑related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.#J-18808-Ljbffr
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