Artificial Intelligence Jobs in San Jose, CA

Owns customer forecasting, demand planning, backlog placement, and revenue execution for major Cloud and AI semiconductor customers. Analyzes infrastructure investments, market trends, capital expenditures, rack-level silicon content, and competitive dynamics to improve forecast accuracy and supply alignment. Coordinates Sales, Finance, Supply Chain, and Business Units, influences strategic decisions, identifies revenue-growth opportunities, and drives execution of customer commitments, supply plans, and risk mitigation.

Leads NVIDIA’s relationships with global energy delivery partners, including systems integrators, service providers, cloud providers, and system OEMs. Develops partner enablement strategies, coordinates proofs of concept and platform porting, supports RFQ responses, establishes joint business goals and go-to-market plans, and connects partner sales teams with NVIDIA for strategic account engagement. The role requires extensive energy-industry, complex sales, partner management, and technical computing experience.

Conduct applied machine learning and NLP research for eBay’s marketplace. Design, train, and evaluate language and multimodal models; analyze datasets; build data pipelines; validate outputs; and integrate research into scalable production systems. Collaborate with engineering and product teams, prototype novel methodologies, document findings, and monitor advances in LLMs and machine learning. A master’s degree in computer science, engineering, or a related field is required.

Leads complex customer-centered UX research for ServiceNow’s AI Control Tower, from discovery through design validation and adoption. Conducts qualitative and quantitative studies, usability testing, surveys, participatory design, and concept testing. Synthesizes user types, personas, journeys, task analyses, and jobs-to-be-done into strategic recommendations for product managers and designers. Provides leadership, mentorship, and direction for collaborative research efforts while championing UX research across the organization.

Supports AI governance projects by assessing risks and controls, documenting processes, gathering requirements, analyzing information, and synthesizing findings. Advises clients on regulatory compliance, risk management, internal controls, and governance improvements. The role involves applying AI/ML governance frameworks such as NIST and ISO, translating technical concepts for stakeholders, collaborating with diverse teams, and developing expertise in corporate governance and risk management.

Lead AI governance engagements by developing governance, risk, and control frameworks; analyzing regulations; managing client projects; mentoring staff; assessing AI/ML lifecycle risks; and advising stakeholders on compliant, transparent, and responsible technology adoption.

Leads enterprise AI governance initiatives, including governance frameworks, risk and compliance programs, controls, regulatory assessments, and responsible AI practices. Advises executive clients, oversees complex engagements, drives business development, develops innovative solutions, mentors teams, and translates technical AI governance concepts into actionable strategies. Requires expertise in AI/ML governance, risk, controls, audit, testing, validation, AI lifecycles, and leading frameworks such as NIST and ISO.

Designs and optimizes AI and machine learning solutions, transforms data into insights, and develops production AI systems. Responsibilities include building data infrastructure, speech recognition and NLP tools, and reusable evaluation and monitoring capabilities. The role independently tests AI systems for performance, robustness, bias, explainability, and failure modes; evaluates technical controls; documents findings; communicates recommendations to clients; and mentors junior team members.

Leads AI and data solution architecture for client transformation programs. Designs generative AI, data modernization, LLM, vector database, RAG, and LLMOps solutions; develops roadmaps and evaluation frameworks; advises executives; drives innovation; manages workstreams, client relationships, business growth, thought leadership, and team mentorship.

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.

Internship opportunities supporting General Intelligence and Robotics Centers. Work may involve foundation models, multimodal pretraining, reinforcement learning, autonomous driving, scalable AI infrastructure, inference systems, robotics, world models, embodied agents, manipulation, locomotion, and whole-body motion control.

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.