cnns jobs in San Diego

Lead AI software strategy and architecture for Qualcomm's automotive platform, directing deployment of computer vision, perception systems, LLMs, and generative AI workloads. Drive architectural decisions across multiple products and customer programs while mentoring cross-functional teams.…See more Ensure solutions meet automotive safety, performance, and reliability standards. Requires 8+ years of software/systems engineering experience with deep expertise in AI/ML, C/C++, and automotive software environments.

The Senior Machine Learning Engineer will design and develop advanced ML models for automotive radar signal processing, including end-to-end pipelines from data preprocessing through model training and optimization. The role requires expertise in deep learning architectures (CNNs, RNNs, Transformers), radar signal analysis, and deploying models for real-time embedded systems with constraints on latency, memory, and power.…See more Responsibilities include evaluating state-of-the-art ML techniques, optimizing models for production, and translating research into scalable algorithms. The position demands a Master's or PhD in a quantitative field, 3+ years of ML research experience, strong proficiency in Python and frameworks like PyTorch or TensorFlow, and foundational knowledge of radar fundamentals.

Design and develop advanced machine learning models for automotive radar signal processing, including end-to-end ML pipelines from data preprocessing through model training and optimization. Evaluate novel deep learning architectures (CNNs, RNNs, Transformers) applied to radar data in time-domain, frequency-domain, and range–Doppler representations.…See more Optimize models for real-time embedded deployment under latency, memory, and power constraints, and translate research outcomes into scalable, production-ready algorithms. Requires a Master's or PhD in a quantitative field, 3+ years of ML research experience, strong radar and signal processing fundamentals, and proficiency in Python, PyTorch/TensorFlow, and cloud platforms.

Design and architect next-generation ML inference infrastructure for globally distributed, multi-tenant model serving with high availability, scalability, and cost efficiency. Lead the development of low-latency, high-throughput inference systems supporting computer vision and multimodal models (CNNs, segmentation, object detection) using Scala, Java, and Go.…See more Build large-scale distributed systems with reactive frameworks, integrate enterprise feature stores, and extend CI/CD pipelines (GitHub Prow, Pulumi) with automation and policy enforcement. Design advanced observability frameworks, optimize ML algorithms for performance, mentor engineers, and ensure MLOps and compliance standards (GDPR, SOC2) across the platform.