Machine Learning Jobs in Sunnyvale, CA

The Principal AI/ML Engineer will lead the AV ML Infra team, overseeing scalable ML infrastructure, project ownership, collaboration, and team mentorship in AI and ML innovation at GM.

As a Senior Machine Learning Engineer, you will manage the entire lifecycle of safety-critical ML perception models, optimize performance, and ensure reliable deployment on embedded systems.

As a Staff AI/ML Engineer, you'll enhance developer productivity, mentor engineers, drive best practices, and implement scalable solutions for AV tools and services.

Defines the technical vision and architecture for NVIDIA’s next-generation autonomous driving stack, integrating classical safety systems with learning-based autonomy. Leads engineering teams across prediction, planning, control, decision-making, and safety; oversees productionization of end-to-end driving pipelines and foundation models; and partners with research teams to bring robotics, embodied AI, and generative AI advances into scalable autonomous vehicle systems.

Advise Product, Engineering, Design, Marketing, and business teams on privacy, data protection, accessibility, consumer protection, copyright, contracts, IP, and emerging AI and autonomy technologies. Lead compliance, safety, patenting, invention harvesting, product integration reviews, and legal process development. Translate legal guidance into actionable product workflows, manage complex cross-functional projects, monitor regulatory developments, review external-facing documents, and support M&A transactions.

Designs and deploys large-scale NVIDIA AI and HPC GPU infrastructure for cloud partners and strategic customers. Leads technical engagements across the customer lifecycle, supports infrastructure development and production, builds networking and compute proofs of concept, troubleshoots performance and scaling issues, and delivers technical presentations and workshops. Partners with sales and business teams to identify opportunities and provides customer feedback to influence product strategy.

Lead Clover-wide AI product vision and roadmap, scaling generative-AI and predictive solutions for merchant commerce. Drive cross-functional delivery, AI governance, MLOps enablement, commercialization, and people leadership to launch and optimize AI-enabled POS and SaaS offerings.

The 12-week MedTech R&D Leadership Development Program internship gives graduate engineering students hands-on project experience, classroom and professional development training, and exposure to R&D platforms. Interns build technical, business, leadership, and cross-functional skills while working on innovative healthcare products and technologies. The program may identify high-performing interns for a full-time leadership program after graduation.

Leads enterprise-wide FP&A, including long-range planning, annual operating plans, forecasts, performance management, executive reporting, scenario analysis, and strategic decision support. Advises the CFO, CEO, executive team, and Board while overseeing finance transformation, planning systems, analytics, AI adoption, governance, and global FP&A talent development. Partners across finance and business functions on resource allocation, cost efficiency, capital deployment, cash flow, and operating performance.

Develop and maintain software for real-time 3D visualization, science simulations, virtual human anatomy, medical simulations, and AI applications. Responsibilities include implementing software solutions from design specifications, studying algorithms and user experiences, troubleshooting and debugging existing applications, and collaborating with cross-functional teams. The role is intended for a junior developer with C++, 3D graphics, machine learning, and LLM experience.

Design and develop reliable, high-performance sensor and compute systems for autonomous vehicles. Build device drivers, communication protocols, runtime APIs, testing and monitoring tools, and onboard software across system layers. Debug complex real-time systems, identify root causes, collaborate with vendors and stakeholders, lead sensor and compute projects, and mentor software engineers.

Design and develop reliable, high-performance sensor and compute software for autonomous vehicles, including device drivers, protocols, runtime APIs, testing, monitoring, debugging, and hardware integration. Lead cross-functional sensor and compute projects, collaborate with suppliers and stakeholders, optimize real-time Linux systems, and mentor software engineers.

Develop and operate machine learning systems for automated manufacturing. Responsibilities include building scalable data and model infrastructure, automating ML pipelines, deploying models to production and edge devices, monitoring performance, optimizing latency, integrating AI/ML tools, managing incidents, ensuring security and privacy, and collaborating with automation engineers, data scientists, data engineers, and software teams.

Lead the reliability product roadmap for Waymo’s autonomous driving system. Define evaluation methods for hardware and software failures, develop onboard solutions and testing strategies, mitigate field issues, and build reliability systems for new vehicle platforms, regions, weather conditions, and operational design domains. Partner cross-functionally with data scientists, engineering, and product teams to establish long-term vision and actionable plans for safe, scalable autonomous driving.

The role involves integrating machine learning and classical trajectory planners within safety frameworks for autonomous vehicles, focusing on architecture, planning, and control algorithms.

Lead development of data pipelines, ETL, and analytics for pre- and post-silicon DFT data; apply statistical methods and applied AI/GenAI to solve EDA/DFT problems; design scalable distributed databases; collaborate across teams and mentor junior engineers.

Design, scale, and optimize a generative conversational AI platform and conversation engine for enterprise use. Build product infrastructure and APIs, improve performance and memory/latency, implement logging/tracing/metrics, enable domain-specific and multilingual experiences, and collaborate with ML, product, and support teams to deliver robust, scalable solutions.

Build and scale Moveworks' conversation engine for enterprise generative AI: design scalable APIs, optimize dialog performance and memory/latency, implement logging/tracing and monitoring, enable multilingual translation and domain-specific experiences, create engineer-facing product infrastructure, and collaborate with ML, product, and support teams.

Scale and optimize a generative conversation engine for enterprise use: build product infrastructure and APIs, optimize dialog performance and memory, enable multilingual support, implement logging/tracing/metrics and debugging tools, and collaborate with ML, product, and support teams to deliver robust, scalable conversational AI.

Lead design and implementation of agentic, conversational AI features for enterprise use cases. Collaborate with ML experts, PM, UX, and GTM to scope, deliver, and scale product-grade LLM/GAI solutions and expand platform reach.

Lead the design and advancement of AI platform infrastructure, focusing on distributed training, efficiency, and optimization of ML models. Collaborate with teams and guide architectural decisions.

The Senior Compiler Engineer will develop a model compilation toolchain for deploying optimized machine learning models in autonomous vehicles, focusing on performance engineering and collaboration with cross-functional teams.

Lead design and development of scalable, high-performance ML training infrastructure. Drive distributed training performance optimization, observability, and developer experience. Own cross-functional infrastructure initiatives, set technical direction and standards, and mentor engineers to deliver platform capabilities that support large-scale model training.

Develop and evaluate ML and perception models for future sensing and autonomous driving. Work on detection, tracking, classification, sensor fusion, training pipelines, validation, robustness metrics, and integrate models into onboard systems while collaborating with cross-functional teams.

Lead technical vision and architecture for ML-driven trajectory generation in autonomous vehicles. Build and deploy scalable training pipelines, integrate models into real-time safety-critical onboard systems, mentor senior engineers, drive cross-functional initiatives, and move solutions from research to production using simulation and large-scale datasets.