Machine Learning Jobs in Sunnyvale, CA

The role involves implementing features for ML data processing, developing workflows for model training and deployment, and supporting ML pipeline operations.

This role involves optimizing inference performance, managing high-performance systems, scaling models, and full-cycle ownership from research to production in a fast-paced environment.

The Solutions Architect will assist customers in adopting NVIDIA's GPU hardware and software, deploying AI/ML solutions, and engaging with technical teams and customers.

Seeking Machine Learning Systems Engineers to build end-to-end 3D native ML systems, focusing on training and inference challenges.

The Software Engineer will develop Nuro's ML Infrastructure focusing on workload orchestration, automated resource provisioning, data processing, and feature management.

The Software Engineer will develop ML infrastructure, enhance resource provisioning, optimize workload scheduling, and manage data processing for Nuro's autonomous vehicle technology.

Design and develop Bird’s Eye View fusion models for autonomous driving using multimodal sensor inputs like LiDAR, focusing on performance, robustness, and scalability.

Develop and deploy machine learning models for agentic workflows, optimize AI systems for multimodal applications, and innovate fine-tuning techniques.

Design and optimize pre-training pipelines for VLM models, conduct research on pre-training techniques, and evaluate performance for autonomous driving.

The Senior ML Performance Engineer will architect a Performance Testing Platform, optimize ML performance on GPUs, and validate model efficiency.

As an AI/ML Engineer, you will design and deploy models for real-world applications, optimize data pipelines, and engage with global leaders to enhance AI technology's impact across sectors like healthcare and government.

Lead and build a research-focused ML team to develop post-training/RL, dataset curation, LLM pretraining, and sparsity techniques; adapt algorithms to Cerebras hardware, run systematic model evaluation, and collaborate with internal teams and external partners to drive production deployments and hardware/software co-design.

The role involves designing AI models and training methods leveraging wafer-scale hardware and collaborating with various teams and partners in AI and computational science.

The R&D Engineer will design and implement performance workloads on Cerebras' wafer-scale hardware, collaborating across teams to advance AI and scientific computing. Responsibilities include optimizing performance models, contributing to the technology roadmap, and publishing research findings.

Translate high-level requirements into hardware features and specify microarchitectures for ML performance. Perform power, area, and performance exploration and optimization. Design high-performance execution units, arithmetic circuits, and programmable engines. Collaborate with verification teams to ensure functional correctness and performance.

Responsible for designing and implementing scalable ML software solutions for Search & Discovery, processing massive datasets, and optimizing ML pipelines for real-time applications.

The role involves designing and developing machine learning infrastructure for annotation, evaluation, and training models, focusing on scalable systems and efficient data workflows.

Join the SOTA Training Platform team to bring up ML models on Cerebras systems, enhancing performance across the software stack and debugging issues.

Build and maintain large-scale user understanding models to improve ad ranking, conversion prediction, and recommendations. Monitor model quality, address data drift, collaborate on long-term direction, and communicate technical work to non-engineers.

The Physical Design Engineer will lead efforts in solving physical design challenges, improve RTL-to-GDS methodologies, and integrate AI/ML solutions into CAD flows, focusing on performance and runtime improvements.

Join the Waymo ML Infrastructure team to build and enhance ML-powered tools and web applications while collaborating with Google to improve developer efficiency.

Join Waymo's Simulator Team to develop ultra-realistic sensor data and perception simulation for autonomous vehicles using advanced ML technologies. Collaborate with foundational AI research teams and drive innovation in simulation realism.

As a Staff Machine Learning Engineer, you will develop and deploy machine learning models for the electric grid, lead projects in planning and reinforcement learning, mentor junior engineers, and collaborate across teams.

The Machine Learning Engineer will design and deploy multi-modal models while collaborating with cross-functional teams to translate research into production-ready solutions, focusing on climate change mitigation.

Lead the Inference ML team in developing tools and APIs for large-scale ML applications, enhancing performance and usability, while collaborating across engineering teams.