Artificial Intelligence Jobs in Sunnyvale, CA

Lead AI FinOps governance to define and track AI cost KPIs, detect anomalies, propose and implement cost optimizations, set spend guardrails with cloud/LLM providers, coordinate cross-functional governance, and present VP-level reporting.

Own and build the agent execution harness and orchestration runtime for enterprise-scale conversational/agentic AI: ensure reliability, observability, prompt/version management, LLM integration, eval frameworks, cost/latency optimization, and provide technical leadership for safe, production-grade agent workflows.

Develop computer-vision and temporal AI/ML models for surgical robotics, including perception of anatomy, instruments, actions, and surgical context from video. Responsibilities include benchmarking architectures, defining perception specifications, managing data and labeling pipelines, building active-learning loops, evaluating offline performance, and deploying models for real-time operating-room use. The role collaborates with AI/ML, robotics, data engineering, software, and clinical teams while advancing research prototypes toward robust products.

Leads legal strategy for NVIDIA’s global supply chain, strategic sourcing, procurement, technology transactions, intellectual property, and infrastructure development. Negotiates complex commercial, engineering collaboration, licensing, and procurement agreements; advises business leaders and stakeholders on high-stakes transactions; and develops scalable legal playbooks, templates, and training. The role requires extensive legal experience, technology transaction expertise, supply-chain sourcing knowledge, strong drafting skills, team leadership, and a US law degree with bar admission or authorization to practice.

Lead full-cycle recruiting for software engineering, infrastructure, platform, solutions engineering, technical leadership, and go-to-market roles. Build proactive talent pipelines, source passive candidates, advise hiring managers on market conditions, manage interview processes, and improve recruiting operations, ATS workflows, reporting, and candidate experience. Partner with executives, HR leadership, and agencies while establishing scalable recruiting practices for a rapidly growing SaaS startup.

Lead Strata from inception as co-founder, defining the vision for regulatory intelligence software, engaging enterprise customers, shaping the product roadmap, providing technical leadership for trusted AI agents, and recruiting teams across go-to-market, product, engineering, and customer success. The role requires early-stage B2B SaaS experience, hands-on LLM and agentic architecture expertise, and the ability to build products emphasizing accuracy, citations, auditability, and expert review.

Own DataVisor’s product marketing, PR, and analyst relations programs. Develop positioning, messaging, thought leadership, product assets, campaign materials, and executive communications while translating complex fraud, AML, AI, and SaaS technologies into customer-focused narratives. Collaborate across Product, Sales, Customer Success, Marketing, and leadership; manage agencies and analyst engagements; support launches and competitive intelligence; and use AI tools and agentic workflows to improve marketing productivity.

Design and build automation tools, dashboards, and systems to deploy and manage cloud-based infrastructure. Collaborate with cross-functional teams to analyze requirements, implement reliable full-stack solutions (React front-end, Go back-end), improve CI/CD pipelines, and maintain production stability while meeting government background/clearance requirements.

Design and build automation tools, dashboards, and cloud-native production systems. Collaborate with cross-functional teams to implement reliable, secure solutions using React and Go, integrate REST/GraphQL APIs, maintain CI/CD pipelines, and leverage AI to improve operations and decision-making.

Design and build highly scalable cloud-native microservices and platform components for AI Detection and Response, ensure reliability through testing and root-cause analysis, integrate with CrowdStrike services/APIs, mentor engineers, and drive operational excellence for production systems handling massive event rates.

Build and scale test automation and contract testing frameworks for AI-driven detection systems across cloud, agent, and browser extension surfaces. Expand unit, integration, contract, and end-to-end coverage, define AI efficacy testing, implement observability and quality telemetry, and collaborate with SRE and engineering to ensure reliable, high-performance SaaS deployments.

Design and build highly scalable cloud-native microservices and platform components for AI Detection and Response. Own architecture, testing, monitoring, and operational excellence; integrate with CrowdStrike services; troubleshoot distributed systems; mentor engineers and drive secure, reliable deployments at scale.

Own end-to-end quality for AIDR services across backend and UI: build reusable test frameworks, validate functional/performance/contract/chaos scenarios, increase automated coverage, mentor engineers, and improve observability and resilience for large-scale cloud systems.

Design and implement highly scalable, cloud-native microservices and platform components for AI detection and response. Build reusable libraries, integrate with CrowdStrike services, ensure reliability and security, drive root-cause analysis, mentor engineers, and deploy/operate services and ML models at scale.

Lead engineering teams to build and ship the AI Control Tower platform: own roadmaps, drive 1.0-to-adoption, embed AI across development lifecycle, recruit and grow engineers, and align cross-functional partners to deliver scalable, cloud-native AI governance and observability for enterprise customers.

Develop computer vision and vision-language models for image and video understanding in restaurant environments. Build datasets, training strategies, evaluations, and benchmarks; diagnose model failures; and deploy efficient, reliable inference pipelines. The role combines research and engineering across scene understanding, object tracking, activity recognition, temporal reasoning, visual grounding, and multimodal models, while partnering with product and infrastructure teams to bring research into production.

Build a personalized provider-recommendation pipeline using LLMs, semantic search, recommendation algorithms, agent workflows, and multi-source knowledge integration. Responsibilities include extracting user requirements, retrieving and ranking providers, connecting backstage decision pipelines, developing evaluation datasets, analyzing failures and feedback, and improving quality, reliability, and latency. The intern will collaborate with engineering and product teams from design through launch.

Lead technical vision and architecture for enterprise ML platforms and MLOps. Design training, feature, serving, and observability systems; establish ML lifecycle and governance; solve complex cross-team engineering problems; mentor senior engineers; evaluate emerging AI technologies; drive cost optimization, compliance, and organizational alignment.

Lead end-to-end client AI transformation engagements: discover business problems with executives, architect and build production-credible PoAs (data pipelines, RAG/LLM apps, integrations), deploy on client cloud, quantify ROI, present to stakeholders, and hand off to delivery teams while mentoring junior engineers and contributing reusable accelerators.

Own the roadmap and lifecycle of AI-powered products, from problem definition through launch and iteration. Partner with engineering and data science to assess feasibility, define requirements and metrics, run experiments, and balance model quality, cost, latency, and user experience. The role requires hands-on AI prototyping or workflow automation, strong communication across technical and business teams, and accountability for product outcomes.

Lead mixed-method UX research for AI-powered products, combining exploratory and evaluative studies with quantitative and qualitative methods. Influence product strategy, collaborate across disciplines, identify customer needs, guide innovation, and elevate research practices. The role requires strong survey design, statistical analysis, interviews, user evaluations, research synthesis, communication, and an AI-native approach to research workflows.

Interns support Nearby AI’s founding team across marketing and operations. Marketing work includes testing creative and landing-page copy, producing local content, tracking organic and AI-search performance, and preparing community partnerships. Operations work includes sourcing and verifying service providers, interviewing homeowners, researching incumbent services, and gathering regional pricing data. Associates must communicate well, perform outbound outreach, use spreadsheets and AI tools, and demonstrate initiative through a completed project or initiative.

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.