Artificial Intelligence Jobs in Sunnyvale, CA

Drive product strategy and execution for Cloud Provider and AI Networking segments. Collaborate with teams to define product requirements, assess market opportunities, and develop go-to-market strategies for Tier 1 and Tier 2 customers.

Assist in deploying and monitoring Cerebras AI infrastructure, perform troubleshooting, collect telemetry, and learn through shadowing senior engineers.

Design, develop, and productionize scalable AI tools for systems engineering in autonomous vehicles, integrating data for traceability, and building backend services.

Build infrastructure for AI/ML environments, collaborate with teams for improvements, and interact with customers to enhance product features.

Lead vision, strategy, and execution for AI-powered support products. Own roadmap, customer discovery, launch, adoption, and continuous optimization. Partner with engineering, data science, UX, and support to deliver generative AI, agent-assist, and autonomous support solutions that improve CSAT, reduce MTTR, increase productivity, and lower operational costs.

Lead strategy, roadmap, and delivery of AI-driven networking products using ML, LLMs, generative and agentic AI. Translate customer needs into product requirements, define KPIs, align cross-functional teams, drive go-to-market and thought leadership, and mentor product managers.

Lead product strategy and roadmaps for inference tools, SDKs, and libraries that enable developers to deploy and optimize GenAI models on NVIDIA GPUs. Partner with internal and external developers, engineering, leadership, and marketing to prioritize features, drive product delivery, and define go-to-market plans focused on performance, cost, and safety.

Serve as an HR Generalist for Microsoft AI, advising leaders on hiring, onboarding, talent development, and organizational effectiveness. Partner with Recruiting and HRBPs, lead people initiatives, use workforce data to inform decisions, and improve employee experience in a fast-growing, agile environment.

Lead an enterprise AI product pod to define use cases, KPIs, scope, and adoption; coordinate cross-functional stakeholders (Legal, Quality, IT/Security); ship LLM/agent/RAG solutions into real workflows; produce reusable Fabric assets; manage roadmap, telemetry-driven adoption, and portfolio reporting.

Design and validate high-bandwidth interconnects for AI clusters across copper and optical media. Define architectures, perform link and channel budget analysis, lead qualification and reliability testing, develop validation plans for transceivers and cables up to 1.6T, and coordinate with suppliers and manufacturing.

The Senior Fullstack Engineer will lead the design and development of scalable software solutions, enhancing product architecture, collaborating with cross-functional teams, and promoting coding best practices.

Design and validate pre-built fraud detection models and cold-start scoring for payments and onboarding. Analyze large-scale consortium data to derive deployable features, test AI agent logic, and collaborate with Product, Engineering, and Strategy to productionize ML and LLM solutions with strong statistical rigor.

Design and optimize large-scale AI platform internals: inference runtimes, model serving, GPU and distributed performance, context engineering, cost optimization, release safety, observability, and production reliability. Provide technical leadership, mentor engineers, define architecture trade-offs, and ensure safe, cost-efficient, and scalable deployment of LLM and agentic systems.

Build and prototype state-of-the-art large language models and AI assistants, take services from ideation to production, fine-tune open-source models, use LLM platforms, analyze model statistics and projections, and collaborate cross-functionally in a fast-paced hybrid Palo Alto environment.

Lead developer relations for NVIDIA's local AI ecosystem: manage strategic partner portfolio, provide technical guidance, drive adoption of NVIDIA accelerated computing and runtimes, define joint plans and success metrics, enable partners with integrations and content, represent partner needs internally, and evangelize local AI at events.

Serve as a technical advisor to research labs to accelerate adoption of NVIDIA AI and computing platforms. Map research ecosystems, track emerging scientific AI trends, collaborate cross-functionally, enable researchers through demos, workshops, and technical content, and represent researcher needs to inform product, education, and platform roadmaps. Attend conferences and foster long-term academic and national-lab partnerships.

Join the Fury Team as an AI Engineer to develop AI models for autonomous systems, designing architectures and integrating ML for real-time applications.

The AI Researcher will lead research in embodied intelligence for defense systems, focusing on reinforcement learning, vision-language modeling, and building scalable AI models for autonomous applications in real-world environments.

Build and productionize AI applications integrating LLMs and vision models into an edge-to-cloud robotics platform. Fine-tune and quantize models, deploy to NVIDIA Orin/Jetson and cloud GPUs, integrate Vector DBs/RAG/agent frameworks, and create data pipelines to collect and version sensor data for retraining.

The AI Accelerator Silicon Architect will define compute architecture, analyze performance, and collaborate with teams on silicon architecture for AI workloads.

Lead and drive design-phase execution for rack-scale AI platforms. Maintain master schedules, track critical-path items and risks, coordinate cross-functional design and readiness reviews, engage CM/JDM partners, ensure validation readiness, and provide executive updates on milestones, risks, and resource gaps.

The role involves developing and productionizing security detection models, collaborating with security research, and shaping engineering culture as a founding team member.

Design, build, and deploy production-grade agentic AI systems and scalable evaluation frameworks. Develop automated metrics to measure accuracy, safety, and reliability of non-deterministic agents. Automate and scale evaluation infrastructure to improve characterization and assessment of onboard autonomous driving software.

Manage full-cycle technical recruiting for Infrastructure, Simulation, and Data Science teams. Source candidates, build pipelines, partner with hiring managers, analyze hiring data, manage candidate experience, extend and negotiate offers, and improve hiring processes.

Build end-to-end Ambient AI products across the full software stack, ship rapidly, work with LLM-powered clinical documentation and billing systems, collaborate with customers, and ensure production reliability (alerting, metrics, SRE).