Artificial Intelligence Jobs in Sunnyvale, CA

Lead technical engagement for AI training and infrastructure at scale. Collaborate with engineering teams and customers to ensure successful deployment and optimization of AI/ML solutions.

Lead design and development of AI/ML framework and inference software (ONNX, ONNX Runtime, Foundry Local). Provide technical leadership, mentor engineers, define requirements and design docs, drive performance, reliability, and observability improvements, act as DRI and participate in on-call operations, and collaborate across products to deliver scalable AI solutions.

Lead the architecture and development of Jerry.ai’s next-generation AI agents and workflow automation platform. Build conversational and computer-use agents, document validation, voicebot interactions, and integrations with external insurance systems. Evaluate emerging AI technologies, deploy production LLM systems, and improve reliability, latency, cost, evaluation, and failure handling. Partner with OpenAI on next-generation voice models and automation capabilities.

Lead the architecture and development of Jerry.ai’s next-generation AI and automation platform. Own conversational agents, voicebots, computer-use agents, backend workflow automation, document validation, transaction orchestration, and external insurance-system integrations. Evaluate emerging AI technologies, deploy production LLM systems, and partner with OpenAI on advanced voice and workflow solutions. The role requires end-to-end software ownership, strong architectural judgment, and experience managing evaluations, regressions, latency, cost, and failure handling.

Lead the architecture and production deployment of next-generation AI agents and workflow automation. Own conversational interfaces, voicebots, computer-use agents, document validation, transaction orchestration, and external system integrations. Evaluate emerging AI technologies, optimize LLM systems for reliability, latency, cost, and failure handling, and partner with OpenAI on advanced voice and automation models.

Owns AI rack- and cluster-scale hardware system architecture for Microsoft Azure infrastructure. Defines requirements and specifications, evaluates architecture tradeoffs across accelerators, networking, memory, storage, power, cooling, and datacenter constraints, and develops proof-of-concepts and performance models. The role drives cross-functional engineering alignment, influences industry standards and vendor roadmaps, and helps deliver scalable AI accelerator systems.

Lead the architecture and development of Jerry.ai’s next-generation AI agents and workflow automation platform. Own conversational interfaces, voicebots, computer-use agents, document validation, transaction orchestration, and integrations with external insurance systems. Evaluate emerging AI technologies, deploy production LLM systems, and partner with OpenAI on next-generation voice and automation capabilities. The role requires end-to-end software ownership, attention to evaluation, latency, cost, reliability, and failure handling.

Develop, test, deploy, and support AI-powered applications, automation solutions, and agent-based systems. Translate stakeholder workflows into scalable technical solutions using Python, C#/.NET, TypeScript, React, Azure AI services, APIs, and orchestration frameworks. Implement RAG, prompt engineering, and agent workflows; maintain testing, documentation, security, monitoring, and support standards. Collaborate with engineering, architecture, legal, security, data, and business teams while contributing to pilots, user adoption, and continuous improvement.

Develop and maintain prompts, responses, and datasets for large language models. Lead labeling initiatives with internal teams and third-party firms, create stakeholder guidelines, and train teams on LLM and dataset development best practices. The role requires native-level English, bilingual proficiency in an additional language, specialized domain knowledge, and a college degree or equivalent experience.

Audit and improve human and model-generated data outputs for accuracy, consistency, and policy compliance. Provide actionable feedback, clarify evolving guidelines, support annotators, coach new team members, monitor workflows and blockers, identify recurring issues and edge cases, participate in calibration discussions, and document findings to improve team performance and quality at scale.

Build and scale the infrastructure layer for visual intelligence: optimize VLM inference and GPU serving, design multimodal APIs and structured outputs, ensure scalable backend systems, reliability, observability, CI/CD, and developer experience. Drive 0→1 product work with strong testing discipline, schema validation, and performance optimization.

Owns the strategy, roadmap, backlog, governance, and performance outcomes for AI and API enablement platforms. Partners with engineering, security, risk, audit, compliance, and enterprise stakeholders to deliver secure, reusable capabilities. Drives platform modernization, automation, developer self-service, adoption, and continuous improvement through metrics and OKRs. Communicates roadmap progress and business value to leadership while supporting API management, cloud, AI integration, and enterprise shared-service initiatives.

The VP of Product Marketing will lead strategy, messaging, and go-to-market initiatives while managing a high-performing team. Responsibilities include competitive intelligence, analyst relations, and sales enablement.

Lead and close complex sell-side software and SaaS transactions, draft and negotiate MSAs, order forms and SOWs, advise Sales and Deal Desk on privacy, security, AI, IP and open source issues, partner with cross-functional stakeholders, improve templates and contracting processes, and provide practical legal guidance balancing risk and commercial objectives.

Leads enterprise transformations that redesign software delivery processes for AI-native development. Analyzes SDLC flow metrics, maps value streams, designs future-state workflows, and builds agentic systems for requirements, design, architecture, and review. Guides engineering teams through AI-augmented ways of working, evaluates emerging AI tools and models, and translates digitally native practices into client roadmaps. The role requires consulting expertise, full SDLC experience, hands-on multi-agent or LLM system development, and executive stakeholder influence.

Develop and maintain end-to-end LiDAR and camera perception software for robotics platforms. Responsibilities include sensor drivers, ROS 2 packages, GPU-accelerated processing, calibration, synchronization, recording and playback tools, Docker environments, packaging, CI/CD, hardware-in-the-loop testing, and production release automation. The engineer will support integration issues and collaborate closely with hardware, silicon, algorithm, manufacturing, and customer 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.

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.

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 technical strategy and architecture for CoreWeave's orchestration platform (SUNK). Own critical orchestration components, drive cross-org initiatives in scheduling, quota enforcement, and hyperscale scaling. Mentor senior engineers, establish reliability and observability best practices, and ensure workloads run reliably and efficiently for large GPU ML training and inference clusters.

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.

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 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.