Artificial Intelligence Jobs in Sunnyvale, CA

Design, build, and deploy LLM-powered validation pipelines and AI integrations for silicon design and post-silicon validation. Lead cross-team AI projects from prototype to production, evaluate emerging AI tech, and measure impact with data systems to improve workflows and scalability across chip development.

Lead founding AI Safety & Security Engineering team, define harness architecture and engineering standards, drive Find/Validate/Patch roadmap, mentor engineers, and translate research into production-grade, secure ML systems.

Design, develop, and deploy enterprise agentic AI applications using LLMs, RAG, AI agents, and orchestration frameworks. Build scalable AI services, models, APIs, and integrations with PLM, SAP, and other enterprise systems supporting engineering and manufacturing workflows. Collaborate with business and IT teams throughout the SDLC, improving solution quality through prompt engineering, evaluation, optimization, and production support.

This role focuses on driving adoption and alignment for ODM partners, supporting data center delivery, and incorporating partner feedback into product strategy.

Develop and formalize methods to validate reported vulnerabilities and generate/verify safe patches for internal NVIDIA targets. Define correctness, regression, and revalidation standards; produce repeatable, auditable processes and evidence. Apply AI-assisted workflows cautiously, perform bounded applied research on vulnerability classes, and document findings for independent security review.

Architect, build, and deploy LLM-powered AI systems and automation to accelerate post-silicon validation and toolchain workflows; integrate AI across engineering teams, evaluate emerging frameworks, measure impact, and operationalize solutions from prototype to production while supporting silicon bring-up and lab debug.

Leads managers of solution sales and solution engineering teams supporting enterprise Cloud and AI customers. Responsibilities include recruiting and developing talent, driving complex technical sales opportunities, coaching executive engagements, improving pipeline conversion and forecasting, aligning resources, and executing sales strategies across Azure, AI, data, analytics, application platforms, and infrastructure.

Design and optimize agentic AI systems for NVIDIA’s CUDA ecosystem, including agent architectures, multi-agent workflows, tool use, memory, orchestration, training, verification, reward, and evaluation systems. Co-design AI systems across models, inference, compilers, runtimes, libraries, kernels, and GPUs. The role requires strong C/C++, Rust, and Python skills, GPU programming expertise, and experience with agent systems, reinforcement learning, or modern AI inference.

Manages software product and engineering activities from definition and planning through execution, production, maintenance, and end of life. Coordinates architects and cross-functional engineering teams, evaluates requirements, validates product definitions, resolves technical gaps, develops product strategies, identifies opportunities, and aligns milestones with business goals. Serves as a central liaison among development, testing, quality, sales, marketing, customers, and executives while overseeing large-scale technical programs.

Lead developer-focused performance optimization for RTX and DGX SoC platforms: collaborate with partners and internal teams to optimize CPU, AI, and 3D-graphics workloads, profile and debug CPU/GPU performance, influence next-gen SoC design, and mentor junior engineers. Occasional travel for conferences and partner site visits.

Serve as the technical sales and customer-facing expert for an AI platform. Responsibilities include conducting product demonstrations, supporting proofs of concept, advising on architecture and development, planning professional services projects, tuning retrieval and agentic pipelines, responding to RFPs, supporting customer adoption, and communicating product feedback internally. The role requires strong AI, cloud, on-premise, application development, storytelling, and enterprise customer engagement experience.

Reviews and scores robotics data episodes using standardized quality rubrics. Identifies failures, unsafe behavior, edge cases, and recurring quality trends; documents findings and provides actionable feedback. Collaborates with stakeholders and teleoperators to improve review workflows, standards, training, and documentation while meeting throughput and accuracy targets.

Design, train, and evaluate Responsible AI components: fine-tune LLMs (including RLHF), build adversarial agent evaluations and safety mitigations, collaborate with engineering to deploy mitigations at scale, monitor operational performance, and troubleshoot end-to-end issues.

Senior Software Engineer focused on AI infrastructure performance insights and observability. Responsible for designing and building monitoring, instrumentation, metrics, and tooling to measure and optimize compute and platform performance while collaborating with infrastructure and ML teams.

Lead and grow a global UX research organization focused on AI-driven product experiences. Define AI-native research strategies, embed modern evaluation and prompt-engineering practices, influence product and executive stakeholders, mentor managers, and translate research into actionable product decisions to improve generative AI workflows and employee experiences.

Source and screen passive technical talent across AI, machine learning, robotics, computer vision, SoC, digital health, and data science roles, including Director-level executives. Develop multichannel sourcing strategies, analyze market data, advise recruiters and hiring managers, build diverse talent networks, promote employer branding, and track recruiting conversion metrics.

Lead design, architecture, and deployment of end-to-end ML solutions for onboard autonomous driving. Apply foundation-model alignment, unsupervised pre-training, imitation and reinforcement learning, and scale large-model training pipelines. Collaborate cross-functionally, mentor engineers, conduct research, and productionize foundation models for safe, reliable real-world driving.

Design, implement, and deploy scalable ML training and evaluation platforms. Own end-to-end projects, make architectural decisions, drive prioritization, collaborate cross-functionally, and mentor engineers to accelerate autonomous driving model development.

Build and deploy scalable ML training and evaluation platforms, drive technical projects and architectural decisions, collaborate with partner teams, prioritize platform improvements, ensure reliability and performance, and mentor junior engineers.

The role involves optimizing large-scale ML infrastructure for autonomous vehicles, conducting analyses, collaborating with teams, and improving system performance.

Join the Inference team to implement features and improvements for model serving on a GPU platform, focusing on performance and reliability.

Senior individual contributor designing and deploying onboard ML solutions for autonomous driving. Develop models (detection, tracking, trajectory, safe AI), build training pipelines and deployment tooling, translate research into production, collaborate cross-functionally, and mentor engineers.

The Staff Software Engineer will lead the architecture and design for AI workload orchestration on Kubernetes, mentoring engineers and driving technical initiatives across teams.

Design, implement, and deploy ML models for autonomous driving (detection, tracking, trajectory generation). Apply advanced techniques (imitation learning, RL, foundation models), collaborate cross-functionally, and support production ML systems and model deployment.

Lead design and delivery of production-grade AI capabilities to improve developer productivity across engineering tools, CI/CD, and embedded software workflows. Define technical vision, integrate AI into toolchains, drive productionization, establish responsible AI standards, mentor engineers, and influence cross-functional roadmaps and implementations.