Machine Learning Jobs in Sunnyvale, CA

Design, build, and deploy ML infrastructure to optimize autonomy models using quantization, pruning, distillation, and model compilers. Maintain and profile GPU ML compilers/runtimes, collaborate with autonomy teams to validate and deploy optimized models to production vehicles.

The Applied Machine Learning Research Scientist will translate ML techniques into scalable systems, improve LLM performance, and optimize workflows while collaborating closely with researchers and engineers.

The role involves developing large-scale data processing pipelines, automating data selection, and collaborating on ML model improvements to enhance vehicle performance and safety.

Develop ultra-realistic simulations for autonomous vehicles using machine learning, focusing on realism and integration into production systems.

Build infrastructure for AI/ML environments, collaborate with teams for improvements, and interact with customers to enhance product features.

As a Senior ML Infrastructure Engineer, you will design scalable architectures for ML, build robust data pipelines, manage GPU clusters, and drive improvements in CI/CD workflows while collaborating with cross-functional teams.

Develop and implement state-of-the-art synthetic sensor simulation methods (photorealistic images, lidar, radar). Train and evaluate ML models for synthetic data, analyze realism and utility, answer sensor-autonomy questions, and collaborate across autonomy, infrastructure, and systems teams. Role scoped as Senior/Staff IC with potential to grow into Tech Lead.

Build and operate production Kubernetes-based compute platform for large-scale data and ML workloads. Improve cluster reliability, GitOps delivery, multi-tenant scheduling, resource isolation, GPU workload orchestration, and reusable batch/workflow systems for petabyte-scale processing while contributing to QMS and continuous improvement.

Build core, shared ML capabilities for a production platform: foundation models for documents, extraction agents, model routing, unified evaluation, and continuous improvement pipelines. Own end-to-end capability development, production deployment, and cross-team support to improve accuracy, latency, cost, and reliability across institutional workflows.

The Machine Learning Engineer role at Wayve focuses on leading initiatives in model-based autonomous driving, involving the development and delivery of ML-driven behaviors, collaboration across teams, and mentoring other engineers.

As a Senior MLOps Engineer, you will optimize machine learning models for performance and scalability, collaborate with R&D engineers, and implement CI/CD pipelines.

The role involves bridging machine learning and systems engineering to enhance autonomy performance by optimizing data quality and developing frameworks for synthetic data in autonomous systems.

Optimize machine learning models for embedded environments, focusing on performance, efficiency, and deployment strategies across various platforms.

Design, build, and improve ML systems and infrastructure, mentor junior engineers, and write maintainable code in a customer-focused environment.

You will build a search and assistant product, improve personalization, train models, and write robust code while mentoring junior engineers.

Optimize inference performance of large multimodal foundation models across cloud and on-robot targets. Diagnose bottlenecks, apply quantization/pruning/distillation, tune kernels (CUDA/Triton), build benchmarking and regression detection, and translate research models into deployment-ready implementations.

As a Staff ML Software Engineer, you will lead the development and deployment of machine learning applications, leveraging your expertise to drive software architecture decisions, maintain cloud infrastructure, and collaborate within a small, agile team focused on geospatial challenges related to climate change.

The role involves designing scalable data pipelines for 3D, video, and sensor data, optimizing infrastructure, and productionizing ML models with researchers.

As a Data Science and Machine Learning Software Engineer at NetApp, you will develop algorithms for real-time personalized search and recommendation using IoT data, while working on cloud-based services and analytics.

As an ML Engineer at Luma AI, you will design high-performance model serving pipelines, manage GPU resources, and optimize CI/CD for large-scale AI systems.

Lead the development of AI/ML deployment infrastructure, collaborate with engineers, develop pipelines, manage teams, and enhance data applications.

Develop AI/ML models for 3D simulation and geology prediction, refine data pipelines, and integrate deep learning tech with product backend.

As an Applied Scientist (ML), you will develop ML solutions, analyze existing systems, and create impactful ML prototypes and evaluation datasets.

You will create large datasets and training recipes, develop methods for data mining, create scalable infrastructure solutions, and collaborate with ML infrastructure teams.

Responsible for software integration and quality for Cerebras AI platform, focusing on automation, debugging, and cross-team collaboration. Develops QA strategies and testing methodologies to ensure product quality.