Artificial Intelligence Jobs in San Jose, CA

Leads product positioning, messaging, go-to-market strategy, launches, segment campaigns, technical content, competitive differentiation, and global sales enablement for FlashBlade//EXA scalable AI and HPC storage solutions. Partners with Product Management, Sales, and Corporate Marketing to drive adoption, revenue growth, and market leadership across enterprise, life sciences, financial services, and academic customers.

Build and deploy full-stack AI/ML and agentic applications for strategic enterprise customers. Partner with technical and executive stakeholders to move use cases from prototypes to production, design scalable AI architectures, establish reusable solution patterns, and advise on AI roadmaps, governance, deployment, and operations. Mentor internal teams, support enablement, and provide product feedback. The role combines remote and customer-facing engagement with occasional travel up to 20%.

Ph.D. interns apply machine learning and AI to semiconductor chip design or enterprise engineering tools. Responsibilities include developing ML models for EDA automation, placement, timing, power, and verification, or deploying LLM, RAG, and agentic systems. Interns will train, evaluate, and deploy models, conduct rigorous experiments, collaborate with engineering and technology stakeholders, assess production performance and safety, and present findings to leadership. The role requires doctoral research in AI, machine learning, computer science, electrical engineering, data science, or a related field.

Lead co-design and build of production agentic and Generative AI systems with strategic ISV partners. Define objectives, prototype and ship RAG, multi-agent, long-horizon workflows on NVIDIA's stack, drive adoption, collaborate with Product/Engineering/Research, and influence product roadmaps. Ensure production inference, evaluation, observability, and enterprise deployment (MLOps/Kubernetes/Docker) with strong technical execution and partner-facing leadership.

Lead technical engagements with strategic ISV partners to design, prototype, and ship production agentic and generative AI systems. Drive adoption of NVIDIA stack (NeMo, TensorRT-LLM, etc.), set account objectives and timelines, coordinate cross-functional teams, and feed partner insights into Product, Engineering, and Research to shape roadmaps and deliver scalable enterprise deployments.

Own the strategy, architecture, evaluation, and production delivery of Jerry.ai’s AI agents and workflow automation platform. Lead chatbot, voicebot, computer-use agent, document validation, transaction orchestration, and external system automation initiatives. Evaluate emerging AI technologies, make technical investment decisions, partner with OpenAI, and collaborate closely with engineering and prompt engineering teams. The role requires hands-on experience launching LLM systems and managing evaluations, regressions, latency, cost, and failure handling.

Own the strategy, architecture, evaluation, and production deployment of Jerry.ai’s next-generation AI agents and workflow automation platform. Lead conversational agents, voicebots, computer-use agents, document validation, transaction orchestration, and external insurance-system integrations. Evaluate emerging AI technologies, make technical investment decisions, partner with engineering and OpenAI, and ensure systems meet requirements for reliability, latency, cost, regression prevention, and failure handling.

Conduct applied research to design, train, evaluate, and deploy AI foundation models and recommendation/personalization systems. Collaborate with cross-functional teams to build models at scale using PyTorch, cloud infrastructure, and open-source tools. Publish research, deliver platform-level code, and drive long-term research agendas to improve customer experiences.

Design and build NVIDIA’s enterprise AI agent platform, including agent blueprints, runtime safety and policy enforcement, multi-agent orchestration, credential brokering, checkpointing, observability, evaluation, and self-improvement loops. Deploy reliable long-running services at scale, integrate enterprise systems, support secure execution, and participate in on-call operations. The role requires extensive distributed systems or infrastructure experience and hands-on agent development.

Designs, develops, tests, and supports ServiceNow automation for ITSM, infrastructure, cloud, endpoint, identity, monitoring, and security operations. Builds workflows, integrations, scripts, catalog automations, and AI-enabled solutions using APIs, IntegrationHub, and MID Server. Integrates ServiceNow with enterprise platforms, improves reliability and operational reporting, maintains CMDB alignment, documents solutions, troubleshoots production issues, and ensures compliance with architecture, security, governance, and release standards.

Leads complex government AI deployments from pre-sale discovery through production adoption, overseeing client relationships, delivery planning, cross-functional execution, change management, and executive communication. Translates agency workflows into technical requirements, manages risks and dependencies, drives measurable outcomes, and converts deployment feedback into product improvements. Builds deployment playbooks, operating cadences, and future delivery teams while supporting renewals and expansions. Requires strong AI/ML and enterprise systems fluency, senior stakeholder leadership, and 30–50% travel.

Develop, optimize, and ship video processing, generative video, and 3D reconstruction algorithms (e.g., NeRF, 3D Gaussian Splatting) across desktop and mobile. Integrate and deploy deep learning models, run research and evaluations, build production-ready modules, and create test environments and tools for runtime verification of video and 3D pipelines.

Designs and operates secure AI applications and backend microservices on AWS. Responsibilities include threat modeling, secure design reviews, vulnerability remediation, secure API development, AI guardrails, prompt testing, RAG pipelines, monitoring, audit logging, and incident readiness. The role collaborates with engineering, product, cloud infrastructure, and security teams to embed secure-by-design practices throughout the software development lifecycle.

As a Customer Success Manager, you will lead initiatives to enhance customer outcomes, manage accounts, and collaborate with engineers to optimize our SaaS platform.

As a Product Marketing Manager, you will lead sales enablement and go-to-market programs, manage product launches, support the revenue team, and collaborate on campaign design.

Lead technical sales engagements with hyperscale, AI platform, data center, and service provider customers. Design scalable AI and data center networks, demonstrate Cisco solutions, address customer needs, and connect architectural vision to account strategy. Apply expertise in routing, optical systems, compute, storage, networking operating systems, silicon, AI workloads, automation, and MLOps. Build customer relationships, lead enablement and demand-generation initiatives, coordinate stakeholders, and drive business outcomes through pre-sales solution development.

Build, maintain, and improve highly available ZoomAI and ML service infrastructure. Own CI/CD, deployment pipelines, zero-downtime designs, monitoring automation, incident investigation, and reliability improvements across cloud and containerized environments.

Leads the design, development, and deployment of scalable, reliable software and infrastructure solutions. Builds AI agents, automation workflows, observability integrations, and monitoring systems using technologies including C++, Python, Java, Claude, Codex, BigPanda, ITMP, and Prometheus. Collaborates across teams, drives engineering standards, mentors junior engineers, and manages complex end-to-end projects.

Build the enterprise platform for an AI creative product, including workspaces, permissions, SSO, audit logs, usage-based billing, invoicing, payment integrations, and security foundations. Develop backend services, APIs, and polished React/Next.js administrative interfaces for multi-tenant customers. Collaborate with founders, sales, and customers to define and ship enterprise features while taking increasing ownership of platform surfaces.

Build the enterprise platform for an AI creative product, including workspaces, roles, permissions, SSO, audit logging, usage-based billing, invoicing, and payment infrastructure. Design scalable APIs and multi-tenant architectures, strengthen security and reliability, and create React/Next.js administrative experiences. Partner with founders, sales, and customers to deliver enterprise requirements, establish engineering practices, and help shape the enterprise engineering team.

Responsible for architecting and developing AI security infrastructure, optimizing performance and scalability of systems, and driving innovation in a collaborative environment.

Lead the development and deployment of AI-powered products with a focus on foundational AI systems, LLM optimization, and team collaboration to enhance user experiences in banking.

As a Lead AI Engineer at Capital One, you will design and support AI software, develop LLM and optimization techniques, and collaborate with cross-functional teams to innovate AI products.

As a Senior Lead AI Engineer, you will design, develop, and optimize AI systems while collaborating with a cross-functional team to implement AI-driven products.

As a Lead AI Engineer, you will design and develop AI components, collaborate across teams, improve performance, and contribute to Capital One's AI vision.