Machine Learning Jobs in Santa Clara, CA

Design, modify, and troubleshoot complex RF/electrical assemblies and schematics. Perform engineering analysis and tests, generate reports and specifications, lead small projects, coordinate suppliers, and implement electrical solutions while mentoring junior colleagues.

Lead the delivery of advanced AI initiatives, manage enterprise engagements, translate business needs into technical requirements, and drive customer adoption.

The Director of AI will lead AI and data engineering teams, define and execute AI strategy, and ensure effective partnership across functions, focusing on scalable AI solutions and responsible AI practices.

As an intern, you will research and develop AI/ML algorithms for computer graphics and robotics in 3D animation, collaborating with a multidisciplinary team.

The intern will conduct applied research in 3D data compression, machine learning, and computer vision, while documenting and submitting findings for intellectual property.

Design, build, and operate petabyte-scale data systems and pipelines that power AI training and evaluation. Focus on ingestion, transformation, quality, lineage, dataset versioning, and high-throughput delivery to training jobs. Optimize data infrastructure for model quality and training efficiency, apply strong software engineering practices, and collaborate cross-functionally.

Design, develop, validate, fine-tune, and deploy AI/ML models (including LLMs and SLMs) and AI agents. Improve model performance using transformers, distillation, and reinforcement learning. Collaborate cross-functionally to build scalable, responsible solutions for delivery intelligence, network automation, edge computing, and DevOps automation.

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.

The Product Development Engineer drives testability and manufacturability of circuits, performs yield analysis using data science techniques, and interfaces with various engineering teams to optimize silicon yield and quality.

As a founding Data Scientist/Machine Learning Engineer, you'll develop AI/ML models, enhance product capability, and drive impactful user outcomes while working closely with product teams.

The intern will design and implement AI-driven automation workflows to enhance the silicon testing process, collaborate with engineers, and document automation recipes.

As a Senior Technical Recruiter, you will design and execute hiring strategies to attract software engineering talent, manage multiple searches, and mentor other recruiters.

Lead a team of engineers in developing offline perception algorithms and data mining tools to produce high-quality data for autonomous driving.

Lead technical vision and strategy for next-generation graphics developer tools. Design and prototype tooling, drive cross-team initiatives, mentor engineers, engage external developers and partners, and deliver production-ready solutions that improve graphics development, debugging, profiling, and optimization workflows.

Lead end-to-end supply planning strategy and governance for networking products (optics, switches, adapters). Build scalable planning frameworks across chips, memory, capacity, and manufacturing; ensure data integrity (BOMs, yields, cycle times); accelerate time-to-market via complexity reduction and AI-enabled planning; and manage and develop global supply planning teams.

The engineer will leverage AI to enhance pre-silicon verification processes, collaborate with teams, and improve methodologies using advanced tools.

Design, build, and deploy production machine learning models for anomaly detection, predictive maintenance, forecasting, and optimization using telemetry from NVIDIA's DGX Cloud. Analyze complex time-series datasets, integrate ML with infrastructure and product teams, develop monitoring tools and simulations, and deliver scalable solutions across distributed cloud environments.

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.

Build and optimize edge and cloud data pipelines for perception ML models. Scale data engine for high-fidelity labels, reduce annotation costs, integrate foundation models for automated labeling and QA, leverage software/hardware-in-the-loop testing, and support DoD field use cases and model deployment lifecycle.