Artificial Intelligence Jobs in San Jose, CA

Lead design and delivery of SMAC workflow methodology and infrastructure to keep system specifications and manufacturing test specs aligned. Build production-grade Python pipelines, automated checks, CI gates, and agent-ready tooling that detect spec drift pre-silicon. Integrate workflows into program milestones, drive cross-org adoption across design, operations, and DFX, and apply AI where appropriate while ensuring reviewable, auditable artifacts.

Lead rebuilding NVIDIA's silicon productization toolchain using applied AI: simulate power and binning, convert silicon behavior into context-engineered AI agents, integrate tools into an agent harness, drive eval-driven development and CI gates, and mentor engineers to deploy LLM-backed production services that protect product quality.

The Solutions Architect will design and build enterprise AI solutions, aid in adopting NVIDIA AI technologies, and work on deep learning applications for customers across industries.

Lead integration of LLMs into ASIC physical design workflows: identify automation targets, build agentic PnR loops, evaluate LLM outputs versus sign-off criteria, create RAG pipelines indexing PDKs and runbooks, write Tcl bridge scripts to translate suggestions into EDA tool commands, and drive prototype-to-production delivery to reduce design cycle time.

Lead GTM strategy and business development for robotics and edge-AI markets in the Americas. Identify use cases, win design-ins, cultivate ecosystem partners, influence product roadmaps, enable field teams, manage stakeholders, track opportunities, and drive multi-year revenue growth and design wins.

The role involves engaging with AI-native companies to optimize their AI platforms using NVIDIA technologies, focusing on architecture, model performance, and collaboration with technical teams.

Design, build, and operate agentic AI and copilot systems for Roku TV: prototype, architect, and deploy end-to-end agents including model selection, RAG/context pipelines, tool-calling, orchestration, observability, testing, and continuous improvement to ensure reliable production performance.

General application to match applicants with Launch Institute of Technology training programs in AI, clean energy, and advanced technology. Collects background, interests, and location to determine program eligibility and availability (remote/hybrid varies by state).

Remotely operate and supervise robotic systems using teleoperation consoles, monitor live video and telemetry, perform real-time task adjustments, collect and annotate operational data, document issues, and provide feedback to engineering for continuous improvement.

Lead signal integrity analysis for photonic-electronic systems, collaborating with design teams to optimize high-speed data paths and package stack-ups.

Build and operate internal AI platform services: chat/agent frontends, retrieval over code/docs, AI-native developer workflows, sandboxed execution, model routing, telemetry, and compliance-minded design for regulated aerospace environments.

Design, develop, and productionize scalable AI systems for autonomous threat detection and response. Build LLM-based reasoning engines, graph-based analytics, streaming feature pipelines, orchestration frameworks, and evaluation/reliability tooling. Collaborate with product, detection engineering, and customer teams to translate attacker behavior into robust ML and rule-based detections.

Lead design and implementation of cloud-native, microservices backend systems that power AI-enabled features (LLM assistants, RAG pipelines, recommendations). Build scalable REST and event-driven APIs, establish MLOps practices, ensure reliability/security/observability, mentor engineers, and collaborate with product, scientific, and infrastructure teams to productionize AI services in regulated scientific environments.

As a Senior Hardware Systems Engineer, you will lead AI rack and cluster infrastructure designs, manage deployments, and collaborate with cross-functional teams to optimize data center solutions.

As a Senior Formal Verification Engineer, you will develop verification strategies and environments for AI SoCs, ensuring the correctness of digital designs through formal verification methods and collaboration with engineering teams.

Lead NVIDIA's storage strategy for AI Cloud, focusing on high-performance storage solutions for critical workloads, mentoring engineers, and influencing technical direction across teams and external partners.

Lead development of autonomous agentic AI tools for threat research and security use cases. Integrate external/internal feeds, build AI-driven security and technical marketing tools, create pipelines for ingestion, observability, testing, and deployment, and deliver dashboards, reports, demos, and reusable assets while collaborating cross-functionally.

Lead hardware-software co-design to enable AI/HPC and agentic AI stacks on RISC-V CPUs. Integrate and optimize low-level kernel infrastructure, runtimes, distributed execution, memory systems, and developer tools, collaborating with architects, compiler engineers, and kernel developers to meet performance targets.

Leads and grows a software engineering team developing cloud software, data platforms, and AI-agentic products. Sets technical direction, guides architecture and design decisions, owns delivery roadmaps, partners cross-functionally, and maintains technical depth for hiring and incident review. Establishes AI-assisted development practices and a strong production reliability and on-call culture while mentoring engineers and supporting career development.

Lead presales for data and AI initiatives: design and present Microsoft Fabric-based data platforms, run discovery and AI workshops, scope proposals, translate technical designs into business value, mentor delivery, and support go-to-market and thought leadership.

Build and maintain an agentic platform that transforms high-stakes medical and legal casework into structured context. Work across the full stack: agent architectures, eval frameworks, data ingestion/indexing, and user-facing tooling. Ship workflows, design reliability-focused evals and guardrails, iterate with user feedback, and set patterns for AI-native development in a healthcare context.

Lead development of the application and cognitive layer for humanoid robots: implement behavior trees and intent-to-action orchestration, build voice applications and multi-modal fusion, integrate robotics capabilities and edge/cloud AI services, and ensure production readiness with CI/CD, telemetry, and monitoring.

Lead development of local AI agent frameworks and runtimes on Windows and GeForce RTX GPUs. Optimize agent inference, sandboxing, and privacy/security policies, collaborate with AI research and driver teams, mentor engineers, and deliver production-ready, local autonomous agent systems.

Develop and deploy LLM workflows and production machine learning models for nuclear-system design and analysis. Build surrogate models for real-time digital twins, manage data pipelines, maintain rigorous information controls, and lead AI initiatives from inception through delivery. The role also involves growing a software engineering team, presenting roadmaps and demos to leadership, gathering user feedback, and collaborating with nuclear engineers in a fast-paced startup environment.

Leads supply chain data science and AI operationalization for Global Service Operations. Converts manual analytics into governed, automated AI-assisted workflows, taking solutions from prototypes to production with lineage, monitoring, lifecycle management, and ownership. Manages analysts, delivers against SLAs, develops statistical and machine-learning models, and communicates actionable insights across procurement, fulfillment, reverse logistics, inventory, and supplier operations. Measures cycle time, quality, and business impact while mentoring the team and advancing enterprise AI governance.