Artificial Intelligence Jobs in Sunnyvale, CA

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.

As an AI/ML Engineer, develop and optimize analytics frameworks to enhance autonomous vehicle development, focusing on automation tools and collaboration with engineering teams.

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.

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.

Build and productionize agentic AI systems and infrastructure (agent orchestration, sandboxed execution, latency optimization, memory, multimodal I/O), collaborate with ML and product teams, mentor engineers, and drive roadmap and scalable platform improvements.

Build and productionize agentic AI systems and infrastructure (agent orchestration, sandboxed execution, memory, knowledge graphs, multimodal I/O). Implement frontier AI algorithms, optimize latency, collaborate across ML, security, product, and mentor engineers to deliver reliable enterprise AI at scale.

Design, train, evaluate, and productionize LLM-based and multimodal agentic AI systems. Improve NLU/NLG, model fine-tuning, evaluation, latency, reliability, safety, data quality, and deployment. Collaborate cross-functionally and iterate on research and production solutions.

Design, build, and productionize scalable agentic AI systems—agent orchestration, sandboxed execution, memory, LLM self-reflection, latency optimization, knowledge graph integration, and multimodal I/O. Mentor engineers, collaborate with ML, security, product, and UX teams, and deliver robust, performant code to advance enterprise AI assistants.

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.

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.

Lead strategy and execution for the Vault data & AI security product bundle (encryption, privacy, log export, code signing, AI Vault console). Define roadmap, drive cross-functional delivery, ensure regulatory compliance, enable customer adoption, and scale monetizable security offerings. Influence engineering, legal, platform security, and executive stakeholders to embed AI-native, automated security capabilities across the ServiceNow platform.

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.

Lead a team building evaluation infrastructure and benchmarks for agentic and generative AI. Design automated tests, validate RAG pipelines, benchmark frontier LLMs, and translate evaluation results into product and risk recommendations to ensure reliable, safe, and cost-effective autonomous workflows.

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.

Build and productionize agentic AI systems including agent orchestration, sandboxed execution, latency optimization, agent memory, LLM self-reflection, enterprise knowledge graphs, and multimodal I/O. Implement frontier AI algorithms at scale, write robust performant code, collaborate cross-functionally, and mentor teammates to deliver reliable enterprise AI capabilities.

Design and productionize scalable agentic AI systems (agent orchestration, sandboxed execution, memory, latency optimization, knowledge graphs, multimodal I/O). Implement frontier AI architectures, write robust production-grade code, collaborate with ML, security, product teams, mentor engineers, and drive roadmap and reliability improvements.

Lead product vision and execution for an MVP autonomous agent platform, prototyping and shipping agentic capabilities. Own roadmap for orchestration, memory, action execution, and secure governance. Drive adoption, define metrics, partner with engineering, GTM, and open-source ecosystem, and translate frontier AI research into scalable platform primitives.

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.

The Sr. Product Marketing Manager will oversee release marketing and PMM operations, coordinating product launches and enhancing processes within the PMM team.

The VP of Product will lead CoreWeave's Research Training Stack, focusing on enhancing AI infrastructure, ensuring quality for pre-trained models, and connecting researchers with advanced cloud capabilities.

Lead architecture and technical strategy for AI-driven product quality systems using LLMs and agents. Build scalable evaluation frameworks, detect regressions, generate insights, and drive cross-functional adoption while mentoring engineers and defining standards for trustworthy AI.

Develop and run rigorous benchmarks and profiles for LLM inference workloads to identify bottlenecks and drive targeted performance optimizations across frameworks, runtimes, and GPU hardware. Design experiments (quantization, caching, speculative decoding), validate changes against real traces, partner with platform engineers to productionize improvements, and produce clear technical writeups to guide model-serving, runtime, and hardware decisions.

Lead product management activities for commercial and internal software products across the lifecycle. Support large enterprise software implementations and program delivery, maintain program controls, report on risks and finances, mentor junior staff, manage stakeholder relationships, and align project objectives with strategic goals while driving continuous improvement.

Lead design and delivery of AI/GenAI solutions and scalable data infrastructure. Drive business development, manage multiple client engagements, mentor teams, implement AI models using Python and C++, ensure data quality, and promote innovation across projects.

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