Artificial Intelligence Jobs in Santa Clara, CA

Leads healthcare operations consulting projects focused on supply chain, process optimization, service delivery, and business transformation. Analyzes performance metrics and business data, implements quality standards, manages change, coordinates operational improvements, and develops execution plans. The role requires client engagement, stakeholder collaboration, critical thinking, and mentoring while aligning healthcare operations with regulations and technology advancements.

Lead design and implementation of Microsoft Dynamics 365 solutions, supervise and mentor teams, manage project delivery, translate complex requirements into technical designs, handle data migrations and integrations, and build strong client relationships while improving service delivery.

Conduct applied AI research and build foundation models across design, training, evaluation, validation, and deployment. Partner with data scientists, software engineers, ML engineers, and product managers to deliver scalable AI products. Research areas include deep learning, graph and sequential models, model optimization, self-supervised learning, robustness, explainability, RLHF, LLM fine-tuning, and data preparation. Own research agendas, publish innovative work, and translate technical findings into business outcomes.

Lead development of scalable, responsible AI platform services, including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize AI training and inference for scalability, cost, latency, throughput, and hardware utilization. Partner with engineers, scientists, technical program managers, and product managers to deliver production AI systems and shape the long-term technical roadmap.

Conduct applied research and develop scalable AI foundation models, including large language models, through design, training, evaluation, validation, fine-tuning, and production implementation. Collaborate with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products. Research emerging methods, optimize training and inference, contribute platform or solution code, and independently pursue impactful research agendas.

Design, develop, deploy, and support foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for scalability, cost, latency, throughput, and hardware utilization. Collaborate with engineers, research scientists, technical program managers, and product managers to deliver responsible AI-powered products and shape the long-term roadmap for foundational AI platforms.

Lead the design, development, deployment, and support of foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Develop optimization techniques that improve production AI system scalability, latency, throughput, cost, and hardware utilization. Partner with engineers, scientists, technical program managers, and product managers while shaping the long-term technical roadmap for responsible AI platforms.

Design, develop, test, deploy, and support foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for performance, scalability, latency, throughput, and cost. Collaborate with engineers, research scientists, technical program managers, and product managers while shaping the long-term roadmap for responsible AI infrastructure.

The Lead AI Engineer will develop and deploy AI components, optimize large language models, and improve AI systems performance while collaborating with cross-functional teams.

Leads engineering for Capital One’s agentic AI platform, overseeing foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, observability, and scalable production deployment. Drives AI architecture, build-versus-buy decisions, optimization for cost and performance, and long-term technical strategy. Partners with research, product, and program teams while recruiting, mentoring, and developing engineering talent.

Leads IT audit engagements assessing digital environments, processes, controls, cybersecurity, data integrity, and AI systems. Manages client relationships, audit teams, project delivery, compliance challenges, data quality validation, governance improvements, and risk management. The role also involves mentoring staff, applying IT governance frameworks, promoting innovative audit technologies, and ensuring assurance standards are met.

Leads the vision, strategy, roadmap, governance, and adoption of ServiceNow’s Lux AI-native experience platform. Oversees product management leaders and partners with executive stakeholders across Product, Engineering, Design, Research, and business units. Drives AI-first experiences, agentic workflows, contextual intelligence, reusable platform capabilities, enterprise scalability, security, privacy, accessibility, compliance, and responsible AI. Represents platform strategy with executives, customers, analysts, and strategic partners.

Leads an engineering team developing AI-native cybersecurity products and capabilities. Defines architecture and technical vision for agentic AI, LLM, retrieval, reasoning, and autonomous security solutions. Guides rapid experimentation from prototypes to enterprise production while ensuring security, reliability, scalability, governance, and evaluation. Builds and mentors highly technical engineering teams, partners with product and security stakeholders, remains hands-on in key technical decisions, and communicates innovation to executives, customers, and engineering organizations.

Build cloud-based AI and machine learning solutions for intelligent enterprise services. Collaborate with developers, product managers, quality engineers, and product owners to design, implement, test, and deliver scalable software. Apply high-performance algorithms, core AI/ML techniques, object-oriented programming, unit testing, profiling, and code tuning to improve user experiences and workflow efficiency.

Own and grow a strategic global OEM account by executing account plans, building senior customer relationships, managing multi-product pipelines, forecasting accurately, achieving revenue and margin goals, identifying strategic opportunities, and representing customer needs internally. The role requires cross-functional influence, global account management, technical sales expertise, CRM discipline, and proficiency with Microsoft Office and Salesforce.

Manage a strategic global AI platform account for Micron, developing growth strategies across embedded, robotics, automotive, client, and consumer markets. Build executive and technical customer relationships, manage multi-product pipelines and forecasts, and coordinate with product, engineering, operations, supply chain, finance, legal, and pricing teams. Provide market intelligence and translate technical requirements into business strategies that drive revenue, design wins, qualifications, and account growth.

Lead AI-powered reporting and insights strategy, design reporting data architecture and pipelines, and implement LLM-powered workflows (RAG, prompt engineering, agentic pipelines). Integrate BI tools and enterprise AI platforms, establish governance for AI outputs, and provide technical leadership to deliver trusted executive insights and scalable self-service reporting.

Leads the discovery, design, prototyping, deployment, adoption, and continuous improvement of AI solutions across Expedia Group. Partners with business and executive leaders to translate ambiguous challenges into measurable outcomes, establishes scalable AI and software engineering standards, evaluates models and architectures, develops reusable frameworks, and coaches senior engineers and emerging technical leaders. Ensures solutions address reliability, security, privacy, cost, observability, evaluation, responsible AI, and sustained business value.

Leads the strategy and execution of AI model performance and on-vehicle inference for autonomous driving. Responsibilities include managing engineering leaders, setting performance budgets, optimizing models and GPU systems, establishing benchmarking and profiling practices, detecting regressions, building performance dashboards, and guiding cross-functional architecture and production decisions. The role also oversees hiring, coaching, staffing, KPIs, technical leadership development, and trade-offs across latency, memory, throughput, accuracy, power, and numerical parity.

Own numerical analysis and model parity for compressed, quantized, and optimized autonomous-vehicle models. Develop Jacobian/Hessian sensitivity tools, connect numerical differences to driving behavior, diagnose training instabilities, and build low-overhead observability for distributed training. Investigate floating-point errors, edge cases, and implementation divergence while translating analytical results into deployment decisions and production-ready tooling.

Design, architect, deploy, and optimize advanced machine learning models for real-time onboard autonomous driving. Lead end-to-end ML projects, improve neural architectures and inference performance, ensure robustness and safety in critical systems, mentor engineers, collaborate across engineering teams, and influence technical roadmaps. The role focuses on foundation models, computer vision, sensor-to-action learning, and onboard autonomy.

Architect and lead production AI products, including chatbots, document processing systems, and agentic workflows. Build FloQast’s centralized AI platform with model routing, vector search, provider management, observability, and MCP integrations. Integrate AI systems with accounting platforms, repositories, APIs, and cloud environments. Establish AI engineering standards, ensure compliance and reliability, and mentor engineers while providing technical leadership across the organization.

Lead development of scalable, reliable continuous integration infrastructure supporting autonomous-vehicle development, machine learning training, simulation, and remote builds. Specialize in Remote Build Execution and a FUSE-based file system, enabling source-code editing and developer workflows. Design and implement productivity improvements, evaluate technologies, influence technical roadmaps, establish engineering best practices, manage technical debt, and mentor engineers while balancing business and customer priorities.

Leads AI engineering enablement by developing onboarding programs, curricula, workshops, technical labs, and engineering forums. Partners with technical experts to identify skill gaps, build practical learning experiences, measure program effectiveness, and improve engineering capabilities. The role requires strong software engineering judgment, hands-on AI/ML expertise, teaching and facilitation skills, and the ability to engage senior technical audiences.

Lead AI Engineer responsible for designing, developing, deploying, and supporting scalable AI systems. The role focuses on foundation model training, LLM inference, similarity search, guardrails, model evaluation, governance, observability, and optimization. Responsibilities include improving AI system scalability, cost, latency, throughput, and hardware utilization; applying emerging research in production; and contributing to the long-term technical roadmap for foundational AI platforms.