Artificial Intelligence Jobs in Sunnyvale, CA

Lead design, build, and operate scalable observability systems for distributed cloud and on-vehicle environments. Contribute code and reviews, create technical designs, build tooling to collect observability signals, run PoCs for new tools, partner with engineering teams, define SLIs/SLOs, guide roadmap decisions, and mentor engineers to improve developer productivity and reliability.

Lead design and implementation of Microsoft Dynamics 365 solutions, supervise and mentor teams, manage project delivery, translate complex requirements into technical designs, handle data migrations and integrations, and build strong client relationships while improving service delivery.

Lead development and operation of CI platform components (RBE, FUSE) to enable scalable build, test, and developer workflows. Collaborate across teams, own projects end-to-end, drive engineering best practices, mentor engineers, perform design/code reviews, and support integration of internal tools with the FUSE-based file system to improve developer productivity for AV and ML workloads.

The Senior AI/ML Capacity Engineer will develop capacity planning models, design analytical tools, and collaborate with teams to optimize costs and efficiencies.

Lead and oversee multiple multidisciplinary teams to architect, develop, deploy, monitor, and maintain full-stack AI solutions (including LLM integrations). Provide strategic and technical leadership, drive experimentation and CI/CD practices, ensure quality and stakeholder alignment, promote innovation, and mentor senior and junior team members to deliver business-aligned AI outcomes.

Develop, test, and validate Generative AI agents and data pipelines with automated testing and governance controls. Analyze complex problems, produce actionable insights, mentor junior associates, and collaborate with multidisciplinary teams to deliver AI-enabled client solutions while upholding professional standards and documentation.

Design, build, and operate production-grade agentic AI systems: multi-agent orchestration, retrieval-grounded reasoning, model integration, observability and governance, and production search/RAG pipelines at enterprise scale.

Design, build, and operate production-grade agentic AI systems that reason over enterprise data, perform automated actions across workflows, and run safely at scale. Responsibilities include multi-agent orchestration, retrieval-grounding (RAG), model integration, observability, trust and governance, and technical leadership across architecture and delivery.

This role involves developing and implementing testing frameworks for ensuring reliability and performance of AI security controls across platforms. The engineer will expand test coverage and contribute to quality metrics for AI models.

Design, build, and deploy scalable ML training and evaluation platforms for autonomous driving. Lead architecture and implementation of distributed, high-performance pipelines, drive cross-team prioritization, mentor engineers, and support recruiting and code quality to accelerate ML model development lifecycle.

Build, test, document, launch, and support AI-powered tools, prompts, skills, agents, automations, and workflows for DC Operations. Partner with field technicians to gather requirements, maintain intake/tracking, evaluate opportunities, facilitate training, provide day-to-day support, track tool usage and impact, and convert field-built solutions into documented, supported assets.

Own the strategy, roadmap, and lifecycle of Aurora’s internal applications supporting autonomous vehicle development. Partner with Engineering, Design, Data, and operations teams to understand user needs, prioritize investments, drive AI-enabled workflows, and deliver scalable products. Translate research and usage metrics into requirements, manage trade-offs and technical debt, mitigate delivery risks, and build stakeholder alignment across complex software ecosystems.

Design, prototype, develop, document, test, troubleshoot, and debug moderately complex software for semiconductor equipment components and devices. Create reusable software modules and libraries, implement bug fixes, analyze requirements, manage project schedules, and communicate with internal and external customers. Apply software engineering, machine learning, and AI concepts to deliver reliable solutions.

Design, prototype, develop, and debug moderately complex software for semiconductor equipment components and devices. Create reusable modules and libraries, documentation, and test procedures; troubleshoot software issues and implement fixes. Collaborate with internal and external customers on requirements, schedules, and troubleshooting while applying software engineering, AI, and machine learning concepts.

Design, develop, debug, and troubleshoot complex software for semiconductor equipment components and devices. Create documentation, test procedures, specifications, and test plans for simulators, test benches, and laboratory environments. Analyze requirements, implement bug fixes, communicate with customers, and support project scheduling. The role applies software engineering, AI, and machine learning expertise to solve customer problems and requires collaboration across technical teams.

Design, develop, debug, and document complex software for semiconductor equipment components and devices. Create test procedures, specifications, and test plans for simulators, test benches, and lab environments. Troubleshoot software issues, implement fixes, analyze requirements, coordinate schedules, and communicate with internal and external customers. The role applies software engineering, machine learning, and AI concepts to semiconductor and display technology solutions.

Design, develop, document, test, troubleshoot, and debug complex software for semiconductor equipment components and devices. Define software specifications and test plans for simulators, test benches, and labs. Collaborate with internal and external customers on requirements, schedules, and troubleshooting, while implementing fixes and contributing to AI and machine learning solutions.

Develop and execute research communications strategies for an AI research institute. Translate complex work involving foundation models, AI systems, papers, benchmarks, open-source projects, and scientific milestones into compelling narratives. Advise leaders and researchers on positioning, messaging, launches, stakeholder engagement, media opportunities, conferences, and external reputation. Build the institute’s profile through influential media, speaking engagements, podcasts, and technical communities while anticipating reputational, technical, and competitive considerations.

Lead Aurora’s enterprise IT architecture strategy, technology roadmaps, modernization, integration standards, AI enablement, and enterprise engineering initiatives. Establish Build-versus-Buy frameworks, assess architectural risks and vendor investments, define secure AI governance and data architectures, and advise executive leadership. Chair the Enterprise Architecture Review Board while partnering with engineering, infrastructure, security, and business applications teams to deliver scalable, secure enterprise systems.

Design and implement foundational data systems for AI, focusing on efficiency and performance at scale. Collaborate on systems that optimize data handling and contribute to research advancements.

Lead engineering and product efforts to design and implement Agentic AI and conversational/web features using LLMs. Collaborate with ML experts, PM, UX, and GTM to scope, deliver, and scale enterprise AI product capabilities across customers and markets while mentoring engineers and shaping product direction.

Lead and scale teams building ServiceNow's AI evaluation platform. Define strategy and roadmap for benchmarks, automated scoring, verifier systems, and quality monitoring. Deliver unified internal and customer-facing evaluation services with APIs and dashboards. Drive novel evaluation methods, safety assessment, and cross-functional collaboration to ensure reliable, production-grade AI quality.

Sell and shape adoption of CoreWeaves AI Runtime Services by driving technical-commercial strategy, proving value with early customers, creating playbooks, and feeding product roadmap. Advise on serving frameworks, batching, execution isolation, throughput modeling, GPU utilization, and SLA structures while partnering with product, infra, and sales teams.

Lead product for Integration Security and AI Gateway Security, defining roadmap and architecture for MCP governance, OAuth-based trust, credential mediation, access control, and audit. Partner with engineering, design, customers, and security teams to translate compliance and zero-trust requirements into developer-friendly features and drive cross-functional delivery and adoption.

Design, build, and operate production-grade agentic AI systems that reason over enterprise data, execute workflows, and run safely at scale. Focus areas include multi-agent orchestration, retrieval-grounded reasoning (RAG), model integration, observability, governance, and technical leadership to ship reliable, low-latency autonomous agents in production.