Artificial Intelligence Jobs in San Jose, CA

Lead regional enterprise sales for NVIDIA’s Physical AI and digital twin solutions. Build executive customer and partner relationships, develop end-to-end solution sales plays, enable global sales teams, manage lighthouse opportunities, track market adoption, and provide customer insights to product teams. The role focuses on industrial manufacturing, factory planning, robotics, synthetic data, intelligent infrastructure, and expansion across APAC and the Americas.

The AI Deployment Engineer will build, deploy, and scale AI solutions while collaborating across departments to ensure successful implementation and support of operational workflows.

Design and build AI-powered cybersecurity platforms that ingest and normalize massive security telemetry, correlate events, predict threats, and generate explainable findings. Develop Python backend services, distributed storage systems, and AIDevOps pipelines for rapid experimentation. Integrate foundation models, agentic systems, RAG, embeddings, and graph-based models into analyst workflows. Collaborate with product teams, security experts, and engineers to deliver scalable, trusted threat detection and automation capabilities.

Design and build AI-native cybersecurity platforms that analyze large-scale security telemetry, correlate events, predict threats, and generate explainable alerts. Develop scalable distributed systems, data pipelines, AI DevOps workflows, agentic systems, RAG and embedding solutions, and model integrations. Collaborate with security researchers, SMEs, product managers, and engineering teams to create trusted automation and analyst-focused threat detection, investigation, and response capabilities.

Design and build AI-powered cybersecurity platforms that process large-scale telemetry, detect and predict threats, and generate explainable alerts. Develop scalable Python services, distributed data systems, AI DevOps pipelines, agentic workflows, RAG capabilities, and model integrations. Collaborate with security experts, product managers, and engineers to validate AI features and improve analyst workflows for security operations.

The role involves designing, developing, and optimizing AI software components, collaborating with cross-functional teams, and contributing to the technical vision of AI systems at Capital One.

Partner with teams to deliver AI-powered products, build foundation models, and conduct applied research to enhance customer experiences.

Lead design, build, deploy, and support scalable, responsible AI systems (foundation model training, LLM inference, similarity search, guardrails, observability). Collaborate with cross-functional teams, optimize model performance and cost, and shape technical vision and roadmap for foundational AI platform services.

Develops technical content, interactive demonstrations, reference architectures, and enablement tools that showcase AI infrastructure and data platforms. Partners with product, marketing, engineering, sales, and partners to translate complex AI/ML architectures, RAG workflows, storage, and networking technologies into compelling technical narratives. Presents at conferences and executive briefings, leads hands-on technical showcases, supports enterprise deals, and channels customer feedback into product and developer-experience improvements.

Lead design, develop, deploy, and support large-scale AI systems (foundation model training, LLM inference, vector search, guardrails, observability). Partner cross-functionally to optimize performance, scalability, and cost, and shape long-term foundational AI technical vision and roadmap.

The role involves leading AI product development, working with cross-functional teams to enhance AI systems, and optimizing large-scale production AI systems.

Lead AI/ML safety engineering for Level 4 autonomous vehicles. Define SOTIF and functional safety strategies, derive requirements for learned systems, design runtime safety monitors and OOD detectors, assess dataset coverage and bias, analyze ML failure modes, and build evidence-backed safety cases. The role requires collaboration across perception, prediction, planning, systems, and safety engineering teams, with onsite work five days per week in Santa Clara, California.

Build and scale an enterprise Retrieval-Augmented Generation platform using Go and Python. Develop full-stack features, backend microservices, frontend interfaces, and AI/ML integrations in partnership with NVIDIA engineering teams. Own features from concept through production, while improving system performance, testing, security, availability, and reliability. Apply hands-on expertise in RAG, LLM pipelines, and AI-driven data retrieval architectures to deliver low-latency enterprise capabilities.

Leads technical architecture and solution design for complex cloud, AI, data center, hyperscale, and WAN networking sales opportunities. Translates customer requirements into scalable solutions, leads demos and proof-of-concepts, prepares RFP responses, and engages senior stakeholders. The role develops architecture playbooks, mentors sales engineering teams, influences deal strategy, and partners with customers, hyperscalers, service providers, and technology partners globally. Travel of up to 50%, including international travel, is required.

Lead development of next-generation AI systems for insurance automation, including document validation, voicebots, computer-use agents, and complex workflow orchestration. Set chatbot architecture and technical standards, build and deploy production automations, evaluate emerging AI technologies, and partner with OpenAI on voice models. Own LLM system reliability, including evaluations, regression prevention, latency, cost, and failure handling.

Build and lead next-generation AI systems for automated insurance quoting, customer support, document validation, voice interactions, and external system navigation. Set technical direction for chatbot architecture, evaluate emerging AI technologies, and deploy production LLM systems. Own automation outcomes end to end, including evaluation, regression prevention, latency, cost, and failure handling, while partnering with OpenAI on voice models and workflow automation.

Leads a multi-year Agentic AI transformation of the Product Development Lifecycle across Product Management, Engineering, and QA. Responsibilities include assessing current processes, designing the future operating model, managing change and executive alignment, driving implementation, defining roadmaps and KPIs, and establishing governance. The role requires strategic thinking, executive influence, cross-functional leadership, project management, and knowledge of software engineering practices within a B2B technology environment.

Develop machine learning and generative AI methods to optimize chip layout for performance, power, and area. Automate physical design, verification, and exploratory PDK generation; build benchmarking, modeling, and QA systems; and evaluate advanced device architectures and process technologies for sub-2nm nodes. Collaborate with global R&D organizations and research consortia, transfer technology to Samsung’s Korea-based development teams, and guide cross-functional technical initiatives.

Provide expert legal and business research to attorneys and staff using advanced research tools; advise on research strategy; manage research workflow; develop training, onboarding, newsletters, and intranet content; evaluate and recommend online resources; support continuous learning and Research Services initiatives.

Build and maintain software that integrates ML/GenAI models with Kubernetes-based edge orchestration. Develop controllers, operators, CRDs, and Helm charts to schedule and deploy AI workloads at the edge, integrate ONNX/PyTorch/TensorFlow models, use AI coding agents, contribute to open-source components, and collaborate across AI, infrastructure, and product teams to ship features end-to-end.

Leads technical and writing teams delivering generative AI products, coordinating customers, data, product, and research teams. Tracks annotator metrics, manages labeling initiatives, creates prompts and responses, develops stakeholder guidelines, and trains teams on LLM and dataset best practices. The role includes customer interaction, quality assurance, and exposure to potentially toxic or sensitive content.

As an Applied Researcher II, you'll develop AI models, collaborate across teams, conduct applied research, and drive innovations in AI to improve customer experiences.

The Applied Researcher I role involves developing AI foundation models, collaborating with cross-functional teams, and conducting advanced research to enhance banking experiences with AI.

Lead design, development, deployment, and support of foundation model training and LLM inference platforms. Build similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, and scalability; contribute to technical vision and roadmap while partnering across engineering, research, product, and program teams.

The Senior Lead AI Engineer will develop and support AI components, lead a cross-functional team, and drive innovation in AI systems at Capital One.