Machine Learning Jobs in Santa Clara, CA

The Senior Machine Learning Engineer will develop and optimize AI image generation models, monitor production issues, and collaborate on ML solutions for generative features.

Design and implement ML solutions for optimizing GPU accelerated Apache Spark workloads. Collaborate on deployment and lead technical teams in ML.

The role involves designing and scaling ML infrastructure for LLMs, including building training pipelines and deploying models in cloud and on-prem environments. Collaboration with engineers and managing GPU/TPU workloads is key.

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.

Engineer will leverage AI for pre-silicon verification, collaborate with teams, develop AI solutions, and engage customers regarding verification strategies.

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

The Principal Compiler Engineer will lead compiler engineering, optimizing ML model performance and coordinating with development teams to innovate compiler infrastructure.

The ML Features Solutions Engineer will develop, optimize, and deploy core machine learning features for enterprise applications while collaborating with cross-functional teams to ensure production-grade quality and performance.

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.

Join a talent network connecting Principal AI/ML Engineers with VC-backed startups. Responsibilities include architecting and deploying production ML/DL models, researching state-of-the-art methods (LLMs, transformers, RL), building scalable training/inference pipelines, implementing robust MLOps, optimizing model performance, and leading technical strategy and teams.

Join a VC-backed startup network connecting Director/VP-level AI/ML leaders to portfolio companies. Lead AI strategy, build and scale ML teams, deploy and improve production ML systems, set model quality and responsible AI standards, partner with product and engineering, oversee data and MLOps, evaluate models and tooling, and communicate strategy to executives and boards.

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.

As a Senior Machine Learning Engineer, you will own the ML stack, develop generation systems, improve quality, collaborate cross-functionally, and build data pipelines.

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.

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

The Machine Learning Engineer will build and scale ML infrastructure, drive model performance, and ensure reliability while collaborating with various teams to optimize feature engineering pipelines and champion best practices.

Build and optimize edge and cloud data pipelines to run perception models, integrate foundation models for labeling and QA, scale data engine architecture, and develop MLOps tooling. Collaborate with DoD customers and use software-in-the-loop and hardware-in-the-loop testing to accelerate model iteration and deployment to edge systems.

Lead design, training, and production deployment of state-of-the-art deep learning perception models (detection, segmentation, tracking, occupancy) for autonomous vehicles. Drive data strategy, corner-case mining, model productization, mentor engineers, and define roadmap for safe, production-grade perception systems.

Research and build efficient ML systems for large-scale LLMs and agentic RL: design algorithms and system techniques, prototype in training/inference stacks, run large-scale experiments, and translate findings into production or publications.

Develop and deploy end-to-end AI/ML solutions focused on GenAI and LLMs. Build and fine-tune models, create embedding search and agentic systems, perform forecasting and data mining, collaborate with stakeholders, and drive improvements in supply chain and material demand forecasting.

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.