Artificial Intelligence Jobs in Santa Clara, CA

The Risc-V Architect will analyze AI workloads, design custom RISC-V cores, and lead the architecture efforts at Tenstorrent, improving CPU performance.

Design and implement high-performance Vision AI pipelines and large-scale distributed services for processing video, image, and 3D data across edge and cloud. Build multi-modal perception, use simulation/synthetic data for testing, profile and optimize GPU-accelerated inference, collaborate with research and product teams, drive code quality and reviews, and mentor engineers to productionize vision models at scale.

Design and oversee datacenter-scale AI infrastructure architectures across networking, compute, storage, and Kubernetes ecosystems. Lead prototyping, testing, performance optimization, and lifecycle refinement for scalable AI products. Collaborate with hardware, software, security teams, customers, and partners to develop proofs of concept and MVPs. Produce technical documentation, user guides, whitepapers, blogs, and curricula while translating complex systems concepts for diverse audiences.

The Innovation Lead will identify, prototype, and commercialize transformative AI, multi-agent, and edge-computing solutions for intelligent networking. Responsibilities include building working proofs of concept, coding and architecting prototypes, partnering with engineering and product teams, engaging customers and partners, influencing product roadmaps, participating in standards and open-source communities, and evangelizing technical innovations through thought leadership and industry presentations.

Designs, deploys, maintains, and tunes large-scale HPC and AI clusters. Responsibilities include workload scheduling, infrastructure automation, CI/CD pipelines, monitoring, logging, alerting, storage and networking deployment, bottom-up troubleshooting, methodology documentation, and R&D support. The engineer works across hardware, operating systems, GPU computing, virtualization, cloud platforms, and software stacks to deliver performant, reliable self-service infrastructure.

Lead product strategy and multi-quarter roadmaps for NVIDIA’s AI Factory software-defined networking stack. Guide upstream development across OVN, OVS, and OVN-Kubernetes; define requirements, priorities, and architecture trade-offs; coordinate dependencies across DPU, fabric, virtualization, and engineering teams; and translate partner and operator needs into product releases, reference deployments, and community strategy.

Supports consulting clients in identifying, prioritizing, and implementing Generative AI solutions. Responsibilities include facilitating workshops, analyzing requirements and processes, supporting proof-of-concepts, exploring data, developing documentation, testing solutions, tracking project plans, translating business needs into technical specifications, and delivering GenAI training. The role emphasizes Microsoft AI technologies, prompt engineering, Agile and SDLC practices, continuous AI learning, and client collaboration.

Provides legal counsel for complex commercial, software, SaaS, cloud, intellectual property, and technology transactions. Advises stakeholders on AI governance, regulatory developments, vendor agreements, data usage, licensing, and emerging technology risks. Drafts and negotiates contracts, maintains legal playbooks, delivers training, and partners with business, engineering, IT, cybersecurity, compliance, procurement, and executive teams on domestic and international matters.

Lead global co-marketing strategies and campaigns with AI ecosystem, Neocloud, and AI ISV partners. Develop joint value propositions, manage partner relationships, coordinate cross-functional teams, oversee budgets and vendors, measure campaign ROI, maintain program governance, enable partners, protect brand integrity, and present results to executives. The role focuses on translating Intel hardware and software capabilities into customer-centric AI and cloud solutions.

Senior DevOps engineer building AI-powered agents, automation pipelines, and full‑stack business applications. Design and maintain integrations (SAP, Salesforce, Google Workspace, Slack, Jira), REST APIs, CI/CD, and containerized deployments. Lead AI standards, prompt engineering, agent frameworks, cross-functional discovery, documentation, and enablement while helping grow an AI Automation & Integrations practice.

Lead applied research on continual learning, fast adaptation, memory, and transfer learning for agentic systems. Own end-to-end pipeline from data generation to evaluation and product integration, run experiments, produce code, and drive research strategy while mentoring and managing a small team.

Develop and test resilient, scalable multi-agent AI systems and RAG pipelines. Integrate agents with external tools and APIs, run LLM/RAG evaluations, write prompts, and support light model fine-tuning to improve network security product development efficiency.

Lead end-to-end delivery of enterprise AI engagements: run discovery, size value, define features, coordinate cross-functional teams, drive adoption and change management, and own commercial expansion and program outcomes in regulated environments.

Design, deploy, and maintain on-prem AI infrastructure and agentic systems. Build GPU clusters, model serving, vector DB-backed RAG pipelines, fine-tune and deploy models, implement governance (MCP), and automate CI/CD and monitoring to integrate AI capabilities into enterprise workflows.

Own go-to-market strategy and positioning for NVIDIA desktop AI products. Lead cross-functional teams through planning, launch, and post-launch optimization. Coordinate OEM and channel messaging, train regional sales, collaborate with product/sales/marketing, and drive analyst/influencer relations to grow pipeline and sell-through.

Own and optimize on-board inference architecture for humanoid robots: map and partition models across heterogeneous accelerators, enforce compute budgets, optimize toolchains and kernels, apply quantization/pruning, profile pipelines, and collaborate with AI, platform, and silicon vendors to meet real-time power, latency, and reliability constraints.

Part-time AI Application Engineer focused on measuring and improving healthcare AI agent performance in production. Responsibilities include instrumenting agent quality, identifying failure points, designing experiments, analyzing results, and partnering with clinical experts to improve patient outcomes. The role requires Python, strong analytical judgment, comfort with ambiguity, and interest in healthcare. Clinical experience is valued but not required. Engagement is structured as a paid internship or contract role with weekly Bay Area office syncs.

Architects RDU-based AI accelerator platforms spanning boards, servers, racks, clusters, interconnect fabrics, power delivery, and thermal systems. Responsibilities include defining specifications, selecting components, evaluating ODM/OEM partners, modeling performance and cost, leading bring-up and qualification, investigating reliability issues, and guiding systems through production and deployment. The role also requires cross-functional technical leadership, executive communication, vendor accountability, and mentoring across hardware, software, performance, and datacenter operations teams.

Evaluates emerging AI technologies, platforms, infrastructure, and vendors through technical assessments, proof-of-concepts, benchmarks, and pilot deployments. Assesses solutions for capability, cost, risk, scalability, and business value; recommends adoption strategies; supports customers and internal stakeholders; delivers technical presentations, workshops, and demonstrations; and monitors developments in LLMs, automation, and agentic workflows.

Leads the productization, deployment, and scaling of perception and physical AI software for robotics and automation. Owns software strategy, architecture, SDK and platform development, field deployment, OTA updates, reliability, and operational excellence. Builds and scales engineering teams across perception, autonomy, infrastructure, and platform functions while partnering with algorithms, hardware, product, systems, applications, and customer teams. Represents software strategy to executives and the board.

Own Lyte’s marketing campaigns and content strategy across social media, blogs, newsletters, press outreach, and thought leadership. Translate complex robotics and Physical AI technologies into clear messaging, support product launches and go-to-market initiatives, manage content calendars, collaborate cross-functionally, and measure campaign performance. The role requires strong writing, technical storytelling, social media expertise, and comfort working autonomously in a fast-moving startup environment.

Lead customer-facing deployments of agentic AI for chip design: onboard and integrate ChipAgents, prototype full-stack solutions (backend models to frontend), collaborate on model and infrastructure improvements, create docs and dashboards, and remain hands-on with code, prompt engineering, and workflow orchestration to drive adoption and iterate from field feedback.

Lead product marketing for AI infrastructure: analyze market and competitive signals, develop positioning and messaging, create content (blogs, webinars, tutorials), support product launches, and partner cross-functionally with engineering, sales, and GTM teams to showcase NVIDIA platform value.

Lead engineering to build and optimize local AI agent frameworks and runtimes on Windows for GeForce RTX GPUs. Ensure secure, sandboxed execution, privacy-aware networking, and efficient local LLM inference. Collaborate with research, driver, and open-source communities, mentor engineers, and produce production-ready code and deployment guidelines.

Lead development of enterprise-sensitive information protection, data access governance, and an AI knowledge platform. Own roadmaps, integrations (email, messaging, document stores), export-control enforcement, audit logging, and cross-functional alignment with Security, Legal, Finance. Mentor engineers and define production readiness for AI agent consumption.