Artificial Intelligence Jobs in San Jose, CA

Lead go-to-market strategy and product marketing for Arm’s Cloud AI portfolio, including AGI CPUs, Neoverse CSS, CPU IP, platforms, and ecosystem solutions. Develop positioning, messaging, customer value propositions, marketing plans, product launches, partner campaigns, content, sales enablement, and executive communications. Collaborate with product, engineering, sales, corporate marketing, and ecosystem partners to translate complex AI and cloud infrastructure technologies into differentiated market narratives and accelerate adoption.

Leads engineering teams building responsible, scalable AI systems and products. Oversees foundation model training, LLM inference, similarity search, guardrails, evaluation, experimentation, governance, observability, and production deployment. Makes build-versus-buy decisions across AI technologies, develops LLM optimization techniques, shapes foundational AI roadmaps, and recruits and mentors engineering talent. Partners with research, engineering, product, and program teams to deliver high-performance AI capabilities.

Design, develop, deploy, and support foundational AI platform components, including LLM inference, model training, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for scalability, latency, throughput, hardware utilization, and cost. Contribute to long-term platform strategy, apply advanced AI research in production, and collaborate with engineers, scientists, program managers, and product leaders while mentoring teams.

Lead the design, development, deployment, and support of foundational AI systems, including LLM training and inference, similarity search, guardrails, evaluation, governance, and observability. Develop optimization techniques that improve scalability, cost, latency, throughput, and hardware utilization. Partner with engineering, research, product, and program teams while contributing to AI platform strategy, technical vision, and long-term roadmaps.

Lead the design, development, testing, deployment, and support of foundational AI systems, including model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale production AI systems for scalability, latency, throughput, cost, and hardware utilization. Collaborate with engineering, research, program management, and product teams while shaping the technical vision and roadmap for responsible AI platforms.

Own the end-to-end product management cycle, including customer discovery, user interviews, solution definition, product requirements, AI prototyping, engineering collaboration, feature launches, and communicating product value. The role focuses on rapidly delivering mobile and web products that improve construction workflows, using data and decisive ownership to address complex customer and operational needs.

Define next-generation data center GPU products by analyzing workloads, especially large language model training and inference. Identify customer opportunities, develop GPU value propositions and product proposals, and collaborate with engineering, operations, field teams, cloud partners, and sales on go-to-market plans, positioning, collateral, and tools. The role requires technical knowledge of GPU, software, and computing architectures, along with 7+ years of technology experience.

Own strategic customer relationships and commercial opportunities for Arm silicon across Physical AI and Edge AI markets. Develop demand, identify lighthouse customers, progress opportunities from evaluation through design wins and deployment, maintain pipeline forecasts, gather market and product insights, and coordinate cross-functional teams. Engage senior business and engineering leaders while communicating Arm’s value in performance, power efficiency, AI capability, security, software ecosystem, and system economics.

Leads customer deployments of integrated hardware and software systems across government and industrial organizations. Responsibilities include deployment planning, customer relationship management, technical proposals, risk mitigation, milestone tracking, roadmapping, demonstrations, presales support, and business development. The role bridges customer stakeholders with engineering and commercial teams while ensuring proposed solutions are successfully delivered.

Design, develop, deploy, and scale enterprise AI products, agents, automations, and intelligent workflows for the People Organization. Build multi-agent systems integrating Workday, ServiceNow, Microsoft Copilot, and other enterprise platforms through APIs and event-driven architectures. Apply HR domain expertise to employee lifecycle processes, ensure secure and responsible AI governance, and implement monitoring, testing, observability, and evaluation frameworks. Partner with business and technology leaders to prototype AI solutions and drive adoption and measurable workforce transformation.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Leads HPE’s global Sales decision sciences organization of approximately 100 professionals across business intelligence, reporting, data architecture, advanced analytics, and AI. The role establishes sales measurement frameworks, executive reporting, predictive and prescriptive decision support, reporting automation, data governance, and a trusted Sales Data Lake. It partners with IT, data governance, finance, marketing, and sales leadership to improve forecasting, seller productivity, business performance, and management decision-making while building a globally integrated analytics organization.

Own the financial services product line from discovery through launch and iteration. Set the roadmap, determine which deployment capabilities become core product features, conduct user discovery, define quality standards, and establish accuracy, human-review, citation, and safe-failure models for regulated workflows. Prototype in design or code, collaborate with engineers and designers, understand the market, and deliver reusable AI capabilities across financial institutions.

Own FloQast’s multi-region AI platform infrastructure across AWS, including Bedrock model runtimes, sandboxed code execution, Terraform, observability, cost controls, CI/CD, security, and reliability. Support Transform, AI Matching, and AutoBuilder by managing scaling, throttling, tenant isolation, journey-based SLOs, model and prompt delivery gates, and AI-specific on-call processes across US, EU, and AU regions.

Lead design, development, deployment, and support of scalable GenAI platform components (foundation model training, LLM inference, vector search, guardrails, evaluation, observability). Optimize training and inference performance, contribute to technical vision and roadmap, and partner with cross-functional teams to deliver responsible, production-grade AI systems.

Lead modernization of enterprise AI/ML infrastructure and analyst workflows. Build agentic, scalable, secure platform services, integrate generative AI and low-code/no-code tooling, scale AutoML for tabular and time-series, and engineer automated governance and controls while mentoring teams and influencing cross-functional stakeholders.

Leads AI adoption and transformation across Finance by identifying use cases, building AI agents and workflow automations, developing reusable AI assets, and delivering training programs. Partners with Finance and technology leaders to drive enterprise adoption, citizen innovation, and workforce capability development. Measures productivity gains, adoption, proficiency, and business value while ensuring solutions meet governance, security, privacy, compliance, and Responsible AI standards.

As an Applied Researcher II, you will create AI systems for banking, working with cross-functional teams to develop and implement AI-powered products, while conducting applied research and engaging with emerging technologies.

Lead a team to deliver AI powered products, leveraging machine learning and NLP for customer applications, while collaborating with cross-functional teams.

The role involves partnering with a team to deliver AI products, utilizing various technologies and conducting applied research to enhance customer experiences leveraging AI.

This role involves leading the development of AI-powered products, optimizing large scale AI systems, and mentoring cross-functional teams. It focuses on designing and deploying AI components, contributing to the technical vision, and applying cutting-edge AI techniques.

Lead development and deployment of foundational AI systems: design, build, test, and support LLM training and inference, similarity search, guardrails, observability, and optimizations for scalability, cost, latency, and throughput while shaping technical vision and collaborating with cross-functional partners.

Lead design, build, deploy, and support of foundational AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, governance, and observability. Optimize production LLM performance (cost, latency, throughput) and contribute to long-term technical vision.

Lead design, development, deployment, and support of foundational AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, and observability. Optimize large-scale AI performance (cost, latency, throughput), partner cross-functionally, and contribute to technical vision and roadmap.

As an Applied Researcher I, you will partner with a cross-functional team to develop AI-powered products, build AI foundation models, and conduct high-impact applied research in machine learning.