Lead a machine learning engineering team designing Visual Language Action Models, Vision Language Models, and Large Language Models for autonomous driving and infotainment on Qualcomm's Snapdragon Ride platform. Optimize deep networks for accuracy-latency trade-offs on resource-constrained edge devices, focusing on low-level perception, end-to-end autonomous driving (L2/L3 ADAS), and multi-modal sensor fusion.… Drive cross-functional collaboration with automotive OEMs and internal Qualcomm teams while building a world-class AI/deep learning engineering organization. Requires 6–8+ years of systems engineering or machine learning experience (depending on degree level), with hands-on expertise in deep learning network design, quantization, and deployment on edge AI accelerators.
vision-transformers jobs in San Diego
Senior Engineer, Machine Learning at Element Biosciences will design, develop, and optimize deep learning models (CNNs, Vision Transformers, U-Net) for biological image analysis and deploy them to production on AWS or imaging instruments. Responsibilities include building end-to-end ML pipelines from data ingestion through inference, applying advanced image processing and computer vision techniques to multimodal biological images, and analyzing single-cell and multiomic data.… The role requires 5–7 years of experience with a Master's degree (or 0–3 years with a PhD) and hands-on proficiency in PyTorch, Python, and cloud deployment, with strong preference for experience in biomedical image modalities.
Design and build large-scale multimodal foundation models that integrate high-dimensional biomedical imaging with molecular and language data. You will implement cross-domain data fusion strategies, develop high-performance ML pipelines for petabyte-scale datasets, and collaborate with scientists and engineers to translate biological complexity into scalable, production-ready code.… The role requires deep expertise in Computer Vision architectures, distributed training frameworks, and expert Python proficiency. You will contribute through high-impact publications or open-source work and demonstrated ability to handle the noise and sparsity inherent in biological data.