Artificial Intelligence Jobs in Sunnyvale, CA

Lead product strategy and execution for an enterprise agentic AI platform. Own agent runtime, MCP connectors, intelligence/data layers, evaluation frameworks, developer experience (SDK/CLI/templates), and governance to enable domain teams to build and promote production-grade agentic applications rapidly while ensuring security, quality, and scalability.

Lead AI adoption across G&A by prototyping LLM/agent solutions, setting technical standards, reviewing code and prompts, partnering with business units to automate complex workflows, and overseeing integrations between AI agents and enterprise systems to deploy scalable, secure solutions.

Lead full-cycle technical recruiting for engineering and AI teams, owning sourcing, interview design, stakeholder advising, and offer management. Build proactive pipelines, advise on market compensation and hiring strategy, champion recruiting operations and AI-enabled sourcing tools, and continuously improve hiring metrics and candidate experience for senior and principal-level technical hires.

Serve as a strategic talent partner advising executives on hiring strategy, organizational design, and workforce planning. Lead full-cycle recruiting for engineering, AI/ML, infrastructure, and leadership roles while improving recruiting operations, sourcing automation, and candidate experience. Coach managers across the employee lifecycle and use talent intelligence to influence business decisions.

Optimize large-scale distributed ML training and petabyte-scale offline inference for throughput and cost. Profile across stack, close performance gaps, improve multi-node scaling and cluster goodput, and build benchmarking and observability tooling while collaborating across teams.

Design and build agentic AI systems and LLM-based solutions for cybersecurity platforms. Implement ML/DL pipelines for threat detection, fine-tune and optimize models, create evaluation benchmarks, collaborate with security researchers and engineers, and deploy production-ready AI-driven SOC, SOAR, and SIEM workflows.

Lead execution of Product Engineering AI and automation initiatives across photonics products. Build and deploy reusable AI/automation tools, transform engineering data into AI-ready systems, and drive cross-functional adoption to improve development, qualification, manufacturing, and reliability processes.

Founding research hire to develop clinical reasoning and computer-use models using proprietary hospital and clinic data. Own end-to-end training and evaluation pipelines, design benchmarks exposing LLM weaknesses on clinical reasoning, run experiments, and publish technical reports and papers. Collaborate closely with founders and deploy research into products that improve physician workflows.

Lead process excellence engagements using AI and automation: assess industry trends and benchmarks, design AI-integrated future-state processes and AX designs, perform process analysis, build data-driven frameworks and change-impact assessments, create AI agents and visualizations, and contribute to domain knowledge and playbooks.

Lead AI system safety assurance for humanoid robots: perform STPA/FMEA hazard analyses, assess model-update risks, define AI safety requirements (inputs, confidence, OOD), develop V&V test cases, deploy validation cells, maintain traceability, and produce evidence packages for certification.

Own model selection, evaluation, and deployment for voice and multimodal restaurant AI agents. Build datasets, experiments, and monitoring to improve accuracy, safety, latency, and cost. Partner with product and engineering to ship model changes with guardrails, rollouts, and clear metrics tied to business outcomes.

Build and optimize conversion-focused web experiences, landing pages, demos, and growth systems. Instrument analytics, attribution, and experimentation; integrate APIs and automate workflows; apply AI to content, personalization, and experiment generation; and partner with Growth, Marketing, and Sales to drive qualified pipeline and measurable revenue outcomes.

Build and operate scalable, secure cloud infrastructure for real-time AI services. Improve reliability with SLOs, observability, capacity planning, and failure testing. Implement IaC, CI/CD, deployment safety, secrets and vulnerability management. Partner with product and AI teams, diagnose distributed failures, reduce cost, and automate platform patterns to enable faster, safer releases.

Build end-to-end AI-driven restaurant products from discovery through production. Own full-stack interfaces, backend services, agent workflows, integrations (POS, reservations, telephony), evaluation systems, and instrumentation. Design agent workflows combining models, rules, memory, and human escalation. Work directly with product, design, and restaurant users to iterate, improve reliability, latency, accuracy, and cost, and ship production-quality solutions.

The role involves designing AI models and training methods leveraging wafer-scale hardware and collaborating with various teams and partners in AI and computational science.

The R&D Engineer will design and implement performance workloads on Cerebras' wafer-scale hardware, collaborating across teams to advance AI and scientific computing. Responsibilities include optimizing performance models, contributing to the technology roadmap, and publishing research findings.

Drive adoption of AI inference platform across large enterprises, own sales cycle, lead technical evaluations, engage executive stakeholders, and shape GTM strategy.

The Senior Staff AI Engineer will lead the design and optimization of reinforcement learning systems for enterprise applications, focusing on scalable architectures and production deployment. Responsibilities include mentoring, collaboration, and ensuring compliance with AI ethics and safety standards.

Lead enterprise sales and go-to-market for NVIDIA AI and accelerated computing with Lenovo: develop technical understanding, package joint solutions, co-market/co-sell across channels, build ecosystem partnerships, and drive integration, commercialization, and thought leadership.

Lead performance engineering for AV and robotics workloads: engage customers, develop parallel algorithms and GPU-optimized libraries, profile and tune GPU kernels, optimize distributed transformer communication using NCCL/NVSHMEM and interconnect strategies, and collaborate with architecture and system teams to influence next-generation hardware and software.

The role involves developing AI applications for VLSI design using machine learning and related technologies. Duties include infrastructure design, AI solution implementation, collaboration with teams, and maintaining design databases.

Design and build safe, auditable agent and autoresearch infrastructure: closed-loop evaluation, isolation and permissioning, experiment orchestration, and large-scale inference/training pipelines. Enable autonomous research loops, evaluator and confidence machinery, and agent runtime features (orchestration, sandboxing, tool frameworks, memory, retrieval). Work end-to-end with engineers to deploy trustworthy LLM-agent capabilities against production systems.

Design and deliver enterprise-grade AI/GenAI solutions on Salesforce or ServiceNow. Lead RAG pipelines, LLM workflows, NLP automation, and vector retrieval integrations. Architect scalable API/microservice integrations, ensure performance, security, and reliability, and define AI governance and guardrails. Collaborate with product, engineering, and customers through discovery, workshops, and demos to operationalize AI for Field and Customer Service workflows.

Design, build, and operate large-scale, low-latency online model inference backend services. Drive inference platform direction, ensure production reliability/observability, manage GCP cloud infrastructure and Kubernetes, and collaborate with ML engineers to scale serving performance.

Support AI product development by conducting user research, experimenting with LLMs and AI models, designing evaluations, translating insights into product requirements, and owning features through development, launch, and iteration using quantitative and qualitative signals.