Artificial Intelligence Jobs in Santa Clara, CA

Field-based Territory Manager responsible for new business development, strategic selling to cardiology/EP stakeholders, full sales-cycle management, territory planning and pipeline forecasting, cross-functional account management, and meeting quarterly/annual sales targets while traveling extensively within a defined geographic territory.

Lead workload-driven AI/ML systems architecture across hardware and software. Own workload characterization, analytical performance modeling, KPI definition, and hardware performance analysis to guide SoC, memory, and HW/SW co-design decisions. Evaluate compute, memory, latency, and software bottlenecks; collaborate with CPU, compiler, runtime, and ML framework teams; present recommendations to leadership; and mentor junior engineers. The role requires expertise in processor and memory architecture, edge AI acceleration, performance modeling, and cross-functional technical leadership.

Participants complete guided AI research activities onsite, including walking, sitting, gestures, photos, audio recordings, and video recordings. The study takes 2.5–5 hours and requires no prior experience. Applicants must be ages 13–75+, speak English, and be authorized to work in the United States. Minors need parental or legal guardian consent.

Design and scale AI-powered sales enablement experiences: architecture for generative AI, RAG, AI agents, content discovery, guided selling, coaching automation, governance, pilots, integrations with enablement/CRM/LMS systems, and measure adoption and business impact.

Develop AI agents that improve productivity across HR, Finance, Product Support, Customer Service, and CAD Design. Build and integrate Retrieval-Augmented Generation models, fine-tune open-source models, collaborate with cross-functional stakeholders, integrate AI into existing systems, and troubleshoot and optimize model performance.

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.

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.

Remotely operate and supervise robotic systems using teleoperation consoles, monitor video feeds and telemetry, execute real-time task adjustments, capture operational data and logs, identify anomalies, support data annotation and quality validation, and provide feedback to engineering for continuous improvement.

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.

The Research Engineer will develop AI algorithms for autonomous systems, collaborating with teams on end-to-end solutions and data-driven models.

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 delivery of scalable AI systems for autonomous threat detection and response. Build and productionize LLMs, graph reasoning engines, streaming feature pipelines, and orchestration frameworks. Own evaluation, reliability, and cross-functional collaboration while mentoring engineers and experimenting with RAG, tool-calling agents, and multi-modal models.

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.

Build and operate cloud-native, large-scale data pipelines and scalable services for the Mist platform. Develop AI-driven routing intelligence to detect, troubleshoot, predict, and mitigate network issues in multi-tenant data centers, collaborating across teams and leveraging AI-assisted development tools.

The Senior Client Engineer leads the design and management of client device solutions, focusing on MDM platforms JAMF and Intune, while mentoring teams and ensuring secure environments.

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 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.

Own the strategy, roadmap, backlog, prioritization, and release planning for an enterprise Generative AI platform. Translate business requirements into scalable platform capabilities and engineering deliverables, while balancing value, usability, security, governance, and technical feasibility. Partner with AI Engineering, Data Science, Security, Privacy, Legal, Infrastructure, and business teams to drive adoption, reusable services, responsible AI practices, and measurable platform outcomes.