Artificial Intelligence Jobs in Santa Clara, CA

Leads engineering teams delivering scalable AI and machine learning solutions on Google Cloud. Oversees generative AI architecture, Vertex AI, LangChain, LangGraph, LLM APIs, RAG, experimentation, evaluation, guardrails, and LLMOps. Guides technical strategy, client alignment, delivery execution, and team development while applying Python, data science, and machine learning methods to build reliable AI products.

Leads Google Cloud AI and machine learning engineering teams and client workstreams. Oversees generative AI, LLM application, agent orchestration, retrieval-augmented generation, model evaluation, prompt engineering, guardrails, and LLMOps initiatives. Reviews architecture, code quality, testing, and solution delivery using Vertex AI, LangChain, LangGraph, Python, and LLM APIs. Coaches team members, improves reusable delivery assets, and manages client relationships, timelines, quality, and expectations.

Develop AI governance frameworks aligned with industry standards, assess AI risk and control programs, interpret regulations and data, and provide actionable recommendations. The role involves client relationship management, translating technical concepts for nontechnical stakeholders, mentoring junior staff, and supporting AI governance policies, controls, audit, testing, and validation across complex regulatory environments.

Leads client-focused generative AI and data modernization architecture, translating business needs into scalable solutions using Google Cloud, Vertex AI, LLMs, vector databases, and modern data platforms. Advises stakeholders, develops AI evaluation and guardrail practices, guides experimentation and deployment, presents technical recommendations, supports proposals, and coaches teams through complex transformation programs.

Design, develop, deploy, and optimize production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead architecture, cost-performance governance, technical reviews, and long-term roadmap development. Apply advanced optimization techniques to improve scalability, latency, throughput, hardware utilization, and inference costs while ensuring responsible AI standards. Mentor senior technical leaders and collaborate with engineers, scientists, technical program managers, and product managers.

Three-month summer internship supporting Cadence EDA applications for silicon, package, and system design. Interns receive training in design, verification, synthesis, timing analysis, physical implementation, analog layout, signoff analysis, embedded software, hardware/software co-design, scripting, and AI-enabled design flows. The role involves collaborating with application engineers and technical experts while developing communication, analytical, presentation, and engineering problem-solving skills.

Lead customer robot deployments from workflow discovery and site assessment through scoping, financial modeling, technical alignment, execution, acceptance testing, training, and handoff. Partner with engineering, research, safety, solution engineering, customers, and external teams to define requirements, success metrics, dependencies, and milestones. Translate field feedback into deployment practices while managing scope changes, implementation tradeoffs, customer value, utilization, and support needs.

Leads NVIDIA’s autonomous vehicle software program portfolio, owning delivery roadmaps, governance, risk management, customer timelines, and cross-functional execution. Manages and develops a team of technical program managers while personally driving complex autonomy programs, integrations, design reviews, and issue resolution. Partners with engineering, testing, regulatory, product, and vehicle-program leaders to deliver autonomous driving features and communicate technical risks and decisions to executives.

Build infrastructure and tooling for AI evaluations, including distributed scoring engines, task-set validation, grader systems, human-review calibration, statistical leaderboards, online feedback capture, and offline-to-online evaluation loops. Partner with product, engineering, data, and ML teams to deliver reliable, observable evaluation workflows that support faster and more trustworthy AI product development.

Leads public relations for NVIDIA’s AI software business by developing compelling technical narratives, pitching reporters, securing media coverage, and building relationships with media and internal teams. The role translates complex AI, software, open-source, and model technologies into engaging stories that strengthen the brand and advance proactive communications strategies across industries.

Designs, develops, deploys, and optimizes enterprise AI systems, including foundation models, LLM inference, agentic workflows, similarity search, model routing, and observability. Establishes AI architecture standards, improves system cost and performance, champions responsible AI, and leads technical direction, mentorship, and cross-functional collaboration across Capital One.

Design and deliver enterprise-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Set architecture and optimization strategies for scalable, cost-efficient, responsible AI platforms. Lead model routing, caching, orchestration, and cloud deployments while mentoring engineers, influencing stakeholders, and advancing AI research into production.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, cost, latency, throughput, and hardware utilization. Define AI architecture, reliability objectives, and technical roadmaps while ensuring security, compliance, and ethical alignment. Lead technical reviews and mentor engineering teams across complex AI product initiatives.

Own product and feature launches, go-to-market communications, messaging, positioning, competitive battlecards, customer insights, content, and sales enablement for enterprise finance automation products. Coordinate with Product, Sales, Customer Success, and Demand Generation while translating complex AI capabilities into compelling messaging for finance buyers, including AP leaders and CFOs.

Plan and lead humanoid robot deployment use cases, build and execute operational and testing plans, and drive product and feature improvements using field feedback. The role owns workflows, roadmaps, team strategy, and customer-facing deployment execution while coordinating high-pressure, hands-on technology development and testing.

Owns CoreWeave’s Physical AI communications strategy, including messaging, media relations, product and customer launches, executive thought leadership, technical content, and reporter relationships. The role translates AI infrastructure, robotics, autonomy, and scientific computing into credible narratives, advises senior leaders, coordinates cross-functional communications, develops customer and partner stories, and measures coverage and message performance.

Leads a global team of approximately 70 employees overseeing corporate IT infrastructure, end-user services, engineering labs, IAM, endpoint management, unified communications, service operations, and technical support. Develops infrastructure strategy, governance, automation, AI-enabled employee services, and scalable XaaS and on-premise solutions. Partners with Product Engineering to improve Zoom products, manages technical debt and vendors, applies security and compliance frameworks, and ensures reliable global support and system performance.

Develop data-centric machine learning solutions for autonomous driving. Design dataset mixtures, curation and mining methods, model-training recipes, scaling studies, and offline evaluations using real and synthetic driving data. Apply self-supervised learning, imitation learning, reinforcement learning, and foundation-model fine-tuning. Train models across large multi-GPU and multi-node datasets, diagnose data-related failures, and collaborate on deploying models into onboard driving systems.

Conduct research on next-generation tiered memory architectures for AI inference and large language model workloads. Develop models, simulations, prototypes, and performance evaluation tools; investigate memory hierarchy, data placement, caching, and migration strategies. Analyze hardware, software, and system-level tradeoffs using hardware platforms, simulation environments, and FPGA prototypes. Document findings, present technical results, and collaborate with architecture teams on future memory technology strategies.

Design, develop, deploy, and optimize large-scale AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead AI architecture, cost-performance governance, optimization, technical design reviews, and responsible AI standards. Partner with engineering, research, product, and program teams while mentoring senior technical staff and shaping the roadmap for foundational AI platforms.

Lead the architecture and development of enterprise AI platforms, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize AI systems for cost, latency, throughput, scalability, safety, and transparency. Set long-term technical strategy, drive organization-wide standards, lead platform initiatives, partner cross-functionally, and mentor senior engineering and research leaders.

Build and deploy scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, GPU/TPU utilization, latency, throughput, and cost. Own complex AI architecture, define reliability objectives, lead technical reviews, ensure security and compliance, contribute to foundational AI roadmaps, and mentor engineers on scalable design and research-to-production practices.

Manage strategic relationships with AI-native startups, drive revenue and quota attainment, qualify and close complex technology solutions, maintain forecasts, and collaborate with solution architects, channel partners, and cross-functional teams. The role requires positioning NVIDIA CUDA-X libraries, supporting startup integrations and go-to-market initiatives, and developing business and technical collaborations.

Owns the design, deployment, governance, and operation of enterprise AI platforms and employee-facing agents. Builds secure control layers including identity, access, device, tenant, MCP, secrets, logging, rate limiting, and cost controls. Establishes AI lifecycle processes, evaluates and rolls out new tools, operates infrastructure to SRE standards, and develops telemetry, dashboards, runbooks, and incident response. Partners with Security, Legal, Privacy, and IT teams to translate governance requirements into enforceable technical controls.

Architects and operates production-grade agentic AI systems for enterprise identity security. Responsibilities include designing multi-agent orchestration, tool use, planning, memory, retrieval-augmented generation, model integration, evaluation, safety guardrails, observability, and human-in-the-loop controls. The role provides technical leadership through architecture ownership, code reviews, mentoring, and establishing scalable production AI practices.