Artificial Intelligence Jobs in San Jose, CA

Develop and maintain prompts, responses, and datasets for large language models. Lead labeling initiatives with internal teams and third-party firms, create stakeholder guidelines, and train teams on LLM and dataset development best practices. The role requires native-level English, bilingual proficiency in an additional language, specialized domain knowledge, and a college degree or equivalent experience.

Lead system-level architecture for connecting XPUs to memory and interconnect fabrics across node, rack, and cluster scales. Define topology and communication strategies for distributed LLM training and inference, resolve integration issues across silicon, networking, memory, and software teams, and mentor system-interconnection architects. The role requires substantial NPU architecture, ASIC tapeout, silicon bring-up, validation, and production deployment experience.

Identify enterprise AI opportunities, evaluate emerging models and platforms, build prototypes and proofs-of-concept, quantify business value, and present recommendations to technical and executive stakeholders to drive AI adoption across the company.

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

Build and scale the infrastructure layer for visual intelligence: optimize VLM inference and GPU serving, design multimodal APIs and structured outputs, ensure scalable backend systems, reliability, observability, CI/CD, and developer experience. Drive 0→1 product work with strong testing discipline, schema validation, and performance optimization.

Manage major infrastructure projects in the energy sector focusing on electric transmission and substation upgrades, ensuring quality and stakeholder satisfaction.

Design, develop, debug, and document complex software for semiconductor equipment components and devices. Create test procedures, specifications, and test plans for simulators, test benches, and lab environments. Troubleshoot software issues, implement fixes, analyze requirements, coordinate schedules, and communicate with internal and external customers. The role applies software engineering, machine learning, and AI concepts to semiconductor and display technology solutions.

The Director of Segment Marketing will drive strategy for cloud infrastructure and financial markets, advocate for customers, build partnerships, and elevate marketing efforts to advance Arm's leadership in data center computing.

Serve as senior technical advisor to strategic partners for Arm-based Edge AI SoC solutions. Lead system-level architecture, IP selection, integration, optimization, technical engagements, demonstrations, and cross-functional resolution to drive design wins and product adoption while providing customer feedback to influence product roadmaps.

Lead and scale teams building ServiceNow's AI evaluation platform. Define strategy and roadmap for benchmarks, automated scoring, verifier systems, and quality monitoring. Deliver unified internal and customer-facing evaluation services with APIs and dashboards. Drive novel evaluation methods, safety assessment, and cross-functional collaboration to ensure reliable, production-grade AI quality.

Lead a team building evaluation infrastructure and benchmarks for agentic and generative AI. Design automated tests, validate RAG pipelines, benchmark frontier LLMs, and translate evaluation results into product and risk recommendations to ensure reliable, safe, and cost-effective autonomous workflows.

Lead AI FinOps governance to define and track AI cost KPIs, detect anomalies, propose and implement cost optimizations, set spend guardrails with cloud/LLM providers, coordinate cross-functional governance, and present VP-level reporting.

Design and build an enterprise generative AI platform: agent orchestration, memory, guardrails, vector search, SDKs, and blueprints. Standardize agentic workflows, optimize orchestration and cost, own Helm/Kubernetes operators, ensure trust and safety, mentor engineering teams, and evangelize platform architecture internally and externally.

Lead design and delivery of an agentic AI platform: define architecture, standardize agentic workflows, build GenAI SDK/CLI, harden guardrails and observability, optimize orchestration and costs, own Helm/K8s operators for autoscaling, incubate RFCs and proofs of concept, and mentor engineering leaders while evangelizing platform practices internally and externally.

The Senior Lead AI Engineer at Capital One will design, deploy, and support AI systems, working with cross-functional teams to innovate and enhance customer interactions while ensuring scalability and performance.

Lead a cross-functional team to deliver AI-powered banking products, focusing on developing and supporting AI software components and optimizing LLM technologies.

Drive new business and revenue growth for commercial insurance through pipeline development, consultative selling, client relationship management, and quota attainment. Use CRM, analytics, predictive insights, and AI tools to prioritize opportunities, manage sales cadences, forecast revenue, and identify upsell opportunities. Develop strategic growth plans, conduct market and competitive research, collaborate with service and marketing teams, and serve as a trusted advisor to clients.

Leads Roku’s global Enterprise Engineering organization across custom applications, integrations, enterprise data, productivity platforms, and AI enablement. Sets technical and architectural direction, establishes cloud-native and secure engineering standards, develops agent-ready systems, drives AI-first engineering adoption, reshapes teams around outcomes, and coaches distributed engineering leaders. Owns enterprise governance, security, audit, compliance, and major cross-system initiatives while influencing executive stakeholders and transforming organizational operating models.

Leads legal strategy for NVIDIA’s global supply chain, strategic sourcing, procurement, technology transactions, intellectual property, and infrastructure development. Negotiates complex commercial, engineering collaboration, licensing, and procurement agreements; advises business leaders and stakeholders on high-stakes transactions; and develops scalable legal playbooks, templates, and training. The role requires extensive legal experience, technology transaction expertise, supply-chain sourcing knowledge, strong drafting skills, team leadership, and a US law degree with bar admission or authorization to practice.

Lead full-cycle recruiting for software engineering, infrastructure, platform, solutions engineering, technical leadership, and go-to-market roles. Build proactive talent pipelines, source passive candidates, advise hiring managers on market conditions, manage interview processes, and improve recruiting operations, ATS workflows, reporting, and candidate experience. Partner with executives, HR leadership, and agencies while establishing scalable recruiting practices for a rapidly growing SaaS startup.

The Staff Field Application Engineer drives adoption of Arm server SoCs, providing technical support, performance analysis, and training to customers while collaborating with internal teams on system integration and optimization.

Lead engineering teams to build and ship the AI Control Tower platform: own roadmaps, drive 1.0-to-adoption, embed AI across development lifecycle, recruit and grow engineers, and align cross-functional partners to deliver scalable, cloud-native AI governance and observability for enterprise customers.

Lead design, build, deploy, and support foundational AI systems including foundation model training, LLM inference, similarity search, guardrails, evaluation, observability, and optimization. Partner with cross-functional teams to deliver scalable, performant, and responsible generative AI platform services and contribute to long-term technical vision and roadmap.

Lead company-wide AI transformation by designing unified distributed AI platforms, integrating LLM ecosystems, and embedding with autonomous driving teams. Build scalable LLMOps and MLOps pipelines, optimize distributed training, inference, GPU utilization, and production latency, and drive adoption of AI-native engineering practices. Provide technical vision, roadmap leadership, and hands-on systems engineering across perception, prediction, planning, control, and simulation teams.

Lead deployment, inference optimization, resource scheduling, profiling, and stability initiatives for AI models across autonomous vehicles and cloud infrastructure. Design low-latency inference pipelines, optimize GPU execution and memory usage, support LLM serving and model validation, diagnose software-hardware integration issues, and collaborate with perception, prediction, cloud infrastructure, and safety teams.