Artificial Intelligence Jobs in Sunnyvale, CA

As a Senior AI Developer Technology Engineer at NVIDIA, you'll design and optimize AI and HPC workloads, solve performance issues, and influence future hardware and software design.

Design and develop a C++-based system for simplifying and accelerating computing in DL and HPC, alongside evaluating performance and improving library quality.

The Research Scientist will develop pioneering generative AI algorithms, design data processing pipelines, and enhance physical AI with physics simulations. The role involves collaboration and innovation that impacts real-world systems.

Develop groundbreaking techniques to accelerate AI workloads on NVIDIA GPUs. Optimize performance of complex AI systems and collaborate with experts in the field.

As an Engagement Manager, you will manage client relationships, lead AI consulting projects, and support business development efforts, all while enhancing enterprise AI adoption.

The role involves developing advanced AI solutions for e-commerce scenarios, designing algorithms, optimizing ML systems, and collaborating cross-functionally to drive innovations that enhance user experience.

Lead the design of a distributed backend streaming engine with a focus on high availability and AI integration, ensuring low latency and high throughput.

Lead and evolve Coupang's core search experience using AI and machine learning, driving innovation, product direction, and measurable business impacts.

Design, develop, and optimize algorithms for oneDNN targeting Intel processors and graphics. Conduct performance analysis and contribute to low-level tuning and optimization efforts, collaborating with cross-functional teams and engaging with the open-source community.

Directs and manages design verification engineers for Subsystem and SoC design, enhances verification processes, and ensures security compliance while leading team performance.

Lead the development of AI applications, ensuring high reliability and performance while collaborating with teams to identify use cases and implement scalable solutions. Drive AI methodologies, governance, and continuous improvement in the organization.

The AI Intern will support the deployment of multimodal models onto vehicle-grade platforms, assist with model optimization, and help ensure reliable performance in autonomous driving applications.

Lead customer-facing software enablement and optimization for Intel products across AI, client, cloud, and graphics. Develop, integrate, test, prototype, and tune software solutions; advise customers; drive product readiness, benchmarking, technical collateral, and workload integration to inform product design and scalability.

Lead rebuilding NVIDIA's silicon productization toolchain using applied AI: simulate power and binning, convert silicon behavior into context-engineered AI agents, integrate tools into an agent harness, drive eval-driven development and CI gates, and mentor engineers to deploy LLM-backed production services that protect product quality.

Lead AI, automation, and digital transformation strategy for Global Services. Define roadmaps, identify automation opportunities, oversee design and delivery of AI-enabled and RPA solutions, ensure regulatory compliance and validation, drive analytics and ROI measurement, manage vendors, and build a high-performing team.

Lead development and implementation of the firm's attorney AI learning strategy. Assess training needs, design curricula, credentialing, and workshops; coordinate cross-functionally with KM, IT, and practice leaders; track participation and outcomes; monitor legal AI tools and translate developments into practical lawyer-facing training and guidance.

Lead frontend development for an AI-driven finance automation platform: build a scalable React+TypeScript design system and interactive UIs (workflow builders, action libraries, observability dashboards, data type management), integrate REST APIs, collaborate with product/design/backend, and own features end-to-end while providing backend support when needed.

Lead product strategy and execution for AI-driven AR/Order-to-Cash offerings. Own roadmap, OKRs, P&L, positioning, and go-to-market enablement. Collaborate with engineering, sales, services, and partners to ship enterprise SaaS products that integrate language models and conversational AI with finance systems. Represent product in pre- and post-sales activities.

Lead design and enablement of scalable enterprise AI architecture and reusable platforms. Define standards, governance, and roadmaps; evaluate Azure/AWS/OpenAI/LLM platforms; guide engineering on cloud-native, API-driven, and distributed deployments while ensuring security, identity, network, and data governance alignment.

Design, build, and deploy scalable Generative AI solutions (LLMs, RAG, agentic AI) in production. Develop pipelines and platform tooling (LangChain/LangGraph, vector DBs), implement cloud-native, containerized infrastructure (Azure/AWS, Docker, Kubernetes, IaC), and contribute to CI/CD, monitoring, security, and operational stability for enterprise GenAI workloads.

Lead architecture for a hierarchical, multimodal embodied-AI system for surgical robotics. Define interfaces between perception, reasoning, and control; ensure real-time, safety-critical deployment; enable offline-to-online data loops; collaborate across research, engineering, data, and product; mentor senior staff.

Lead applied AI research teams to define multi-year roadmaps, prototype and transition research models into production, build surgical data pipelines, ensure regulatory and privacy compliance, align AI with product and business goals, and collaborate across clinical, engineering, and commercial stakeholders to commercialize medical robotics AI capabilities.

Design, train, and evaluate computer-vision perception models (anatomy, instruments, actions) for surgical video; develop temporal/video models and bench-mark SOTA; define perception I/O and move models from offline experiments to robust, real-time OR performance; establish continuous improvement loops, annotation pipelines, and partner cross-functionally to enable prototype-to-product deployment.

Design, build, and operate large-scale ML training and evaluation infrastructure for RL/AI research. Enable distributed, cost-aware GPU training, benchmarking, data curation, and continuous evaluation; collaborate with research and platform teams to productionize models.

Build and productionize scalable backend services, APIs, and AI workflows to connect requirements, modeling, validation, and test data for autonomous vehicle programs. Implement traceability, versioning, dashboards, and KPIs, refactor prototypes into certifiable systems, and collaborate with systems engineering, safety, and autonomy teams to support compliance and validation at scale.