Artificial Intelligence Jobs in San Jose, CA

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

The Head of AI Applications will define and execute the AI strategy within SiTime, leading the development and deployment of AI solutions across various business functions while driving adoption and managing a high-performing team.

The role involves collaborating on AI GPU deployment, optimizing performance, mentoring engineers, and improving user experience on NVIDIA RTX platforms.

Drive deployment of NVIDIA GPU and networking solutions at customer data centers: advise on network/compute/storage design, perform bring-up visits, debug performance, build POCs/demos, and liaise with product, engineering, and sales teams.

The Senior Systems Software Engineer will optimize AI stack readiness for the DGX Station, focusing on application performance, DL framework analysis, system-level optimization, and collaborating with cross-functional teams to enhance multi-GPU capabilities and ensure product effectiveness.

Develop tools for AI workload profiling and debugging in GPU clusters, collaborating with various teams to enhance performance and efficiency.

The Developer Relations Manager will engage research labs to promote NVIDIA's AI platforms, assess institutional opportunities, provide technical guidance, and connect with researchers to drive product development and collaboration.

Design and deliver high-performance, area- and power-efficient ASIC/GPU micro-architecture and RTL. Develop agentic AI solutions to craft micro-architecture, implement RTL, and produce fully verified, synthesis/timing-clean designs. Collaborate with designers and tool owners, and build LLM-powered engineering assistants, multi-agent systems, and multi-modal dialogue tools to accelerate chip design workflows.

The Staff Software Engineer will lead the development of cloud-native AI platforms, optimizing AI workload deployment in Kubernetes environments and collaborating with cross-functional teams.

Design, build, and deliver an AI and machine learning platform, enhancing developer velocity while collaborating with cross-functional teams.

As a Staff AI Research Engineer, you will develop AI solutions, implement machine learning algorithms, benchmark techniques, and transition prototypes to production.

Design and evaluate machine learning algorithms focused on autonomy and perception, prototype solutions, conduct experiments, and transition prototypes into production.

Lead end-to-end silicon diagnostic pipeline using AI/ML to analyze scan failures and process data, build models for failure classification and predictive yield, orchestrate AI-based EDA tools, and drive cross-functional root-cause analysis to improve yield and time-to-market.

Build and operate regulator-grade, production AI-enabled full‑stack applications: real-time audit dashboards, CAAT/continuous-monitoring surfaces, and agentic human-in-the-loop apps. Own end-to-end stack from data modeling and APIs to React/Next.js UIs, ensure responsible-AI practices, data protection, audit trails, and drive adoption with non-technical auditors.

Quota-carrying sales executive responsible for sourcing, developing, and closing technology consulting engagements focused on AI. Manage full sales cycle from prospecting to proposal and contract negotiation, build and maintain pipeline, collaborate on go-to-market strategies, engage executive-level buyers, and participate in marketing events. Hybrid role with travel (~20-25%).

Enable end-to-end agentic AI GPU deployments on NVIDIA RTX and DGX by profiling and optimizing performance, contributing to open-source LLM/GenAI projects, delivering trainings and sample code, collaborating with drivers/architecture teams, and mentoring junior engineers.

Develop and maintain XR products and spatial UIs to interface with humanoid robots and data-capture hardware. Implement features, fix bugs, harden XR data sync, integrate custom hardware with XR headsets, and support hardware experiments, low-latency video streaming, and motion-capture workflows.

Contribute to AI research in LLM post-training and reinforcement learning, developing scalable systems for Zoom's AI Companion. Engage in innovative research, implement model architectures, and partner with teams to translate prototypes into production systems.

Architect, prototype, and deploy production-grade agentic and multi-agent AI systems for Media & Entertainment. Build high-performance RAG pipelines over multi-modal assets, optimize GPU inference and TCO on NVIDIA platforms, advise customers pre/post-sale, and create reusable reference architectures, blueprints, and technical collateral to scale solutions.

Lead design and implementation of Workday HCM and Compensation solutions. Collaborate with HR and IT stakeholders, provide technical leadership, optimize Workday architecture, troubleshoot complex issues, and drive continuous improvement and adoption of best practices.

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.

Lead technical customer engagements to design, build, and deploy GPU-accelerated AI/ML, DL, and data analytics solutions on cloud platforms. Create PoCs, run demos, collaborate with sales and engineering, deliver workshops and technical presentations, and troubleshoot customer deployments at scale.

Own the end-to-end product lifecycle for agentic AI features within an enterprise automation platform. Define roadmaps, engage customers for discovery and validation, partner with engineering/UX/applied AI to ship products, create PRDs and success metrics, track agentic AI research/trends, and support go-to-market execution.

Own reliability, observability, and delivery for a cloud-native, multi-tenant Kubernetes platform. Build CI/CD, GitOps, IaC, and AI-first automation for incident response and LLM infrastructure. Instrument SLOs and tracing, harden secrets and supply-chain security, troubleshoot production issues, and mentor engineers in AI-driven operations.

Architect and build production-grade AI applications and shared platform services across desktop, mobile, web, and cloud. Lead full-stack engineering, CI/CD, observability, and operational excellence. Build foundations like agent orchestration, memory/context services, evaluation frameworks, telemetry, and workflow execution. Operationalize NVIDIA AI technologies, collaborate with cross-functional teams, mentor engineers, and drive architectural alignment to deliver scalable, reliable enterprise AI products.