Artificial Intelligence Jobs in Santa Clara, CA

Develop and productionize agentic engineering systems for a physical-world AI agent. Build and maintain the agentic harness and tool integrations, run evaluations, analyze production traces, debug failures, collaborate with research to create and deploy custom models, and incorporate new agentic frameworks into the product.

Develop and test resilient, scalable multi-agent AI systems and RAG pipelines. Integrate agents with external tools and APIs, run LLM/RAG evaluations, write prompts, and support light model fine-tuning to improve network security product development efficiency.

Develop and optimize state-of-the-art deep learning and ML techniques for current and next-generation GPUs. Analyze and tune performance, provide GPU-based AI solutions to customers, and collaborate with architecture, research, libraries, tools, and system software teams to influence future hardware and software designs.

Design, deploy, and maintain on-prem AI infrastructure and agentic systems. Build GPU clusters, model serving, vector DB-backed RAG pipelines, fine-tune and deploy models, implement governance (MCP), and automate CI/CD and monitoring to integrate AI capabilities into enterprise workflows.

Senior DevOps engineer building AI-powered agents, automation pipelines, and full‑stack business applications. Design and maintain integrations (SAP, Salesforce, Google Workspace, Slack, Jira), REST APIs, CI/CD, and containerized deployments. Lead AI standards, prompt engineering, agent frameworks, cross-functional discovery, documentation, and enablement while helping grow an AI Automation & Integrations practice.

Architect and optimize timing and power implementation flows for ASIC/FPGA designs. Lead STA and UPF-based power flows, library generation/characterization, automation infrastructure, and apply ML/AI to improve flow efficiency, debug productivity, and signoff QoR while collaborating across PD, STA, library, and CAD teams.

Lead and grow a hands-on AI Products engineering team building LLM and agent capabilities. Own technical vision, evaluation practices, and production delivery. Split time between management and coding, drive 0-to-1 product work, and ensure model quality, reliability, and upgrade pipelines.

Lead vision and strategic architecture for enterprise AI and analytics on Everpure's all-flash platform. Define data/metadata orchestration, build ecosystem partnerships, drive technical competitive strategy, and act as spokesperson to executive leadership and customers to enable AI-ready, governed data pipelines across edge, core, and cloud.

Own and optimize on-board inference architecture for humanoid robots: map and partition models across heterogeneous accelerators, enforce compute budgets, optimize toolchains and kernels, apply quantization/pruning, profile pipelines, and collaborate with AI, platform, and silicon vendors to meet real-time power, latency, and reliability constraints.

Own go-to-market strategy and positioning for NVIDIA desktop AI products. Lead cross-functional teams through planning, launch, and post-launch optimization. Coordinate OEM and channel messaging, train regional sales, collaborate with product/sales/marketing, and drive analyst/influencer relations to grow pipeline and sell-through.

Build and maintain end-to-end mobile trading systems for iOS and Android. Ship production-quality, well-tested code, apply blockchain and crypto knowledge, and integrate AI tools to improve engineering workflows. Break down projects, collaborate cross-functionally, conduct and act on code reviews, mentor teammates, and participate in hiring and onboarding. Influence product decisions through data-driven insights and provide domain expertise for mobile trading initiatives.

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).

Lead development and validation of LLM inference platforms for NVIDIA edge GPUs. Track open-source framework innovations, analyze model-to-hardware mapping, optimize GPU kernels and multi-node inference, produce performance reports, maintain developer inference recipes, own model bring-up workflows, and support partners on hardware-specific inference issues.

Lead development of AI enablement roadmap, build state-of-the-art AI systems, and produce tools, services, and blueprints to accelerate AI adoption across NVIDIA. Collaborate with cross-organizational leaders and external partners to optimize productivity for software, hardware, and other workflows.

Design and implement AI-driven optimizations for software development workflows, leveraging LLMs and Agentic AI to enhance productivity and reliability.

Lead engineering to build and optimize local AI agent frameworks and runtimes on Windows for GeForce RTX GPUs. Ensure secure, sandboxed execution, privacy-aware networking, and efficient local LLM inference. Collaborate with research, driver, and open-source communities, mentor engineers, and produce production-ready code and deployment guidelines.

Research, prototype, and implement methods to improve efficiency and deployability of LLMs, VLMs, multimodal models and agents. Focus on compression, quantization, inference and reasoning optimizations, novel architectures (MoE, SSMs, Looped Transformers), gradient-free methods, and inference-time optimizations. Build evaluation pipelines, run benchmarks, and contribute to publications, open-source, and IP.

Lead development of enterprise-sensitive information protection, data access governance, and an AI knowledge platform. Own roadmaps, integrations (email, messaging, document stores), export-control enforcement, audit logging, and cross-functional alignment with Security, Legal, Finance. Mentor engineers and define production readiness for AI agent consumption.

Analyze, profile, and optimize AI training workloads on NVIDIA GPUs across the hardware/software stack. Implement production-quality software, build MLPerf training submissions, model workloads in simulators, and develop automation tools to identify and resolve performance bottlenecks.

Lead customer-facing deployments of agentic AI for chip design: onboard and integrate ChipAgents, prototype full-stack solutions (backend models to frontend), collaborate on model and infrastructure improvements, create docs and dashboards, and remain hands-on with code, prompt engineering, and workflow orchestration to drive adoption and iterate from field feedback.

Lead product marketing for AI infrastructure: analyze market and competitive signals, develop positioning and messaging, create content (blogs, webinars, tutorials), support product launches, and partner cross-functionally with engineering, sales, and GTM teams to showcase NVIDIA platform value.

Design, build, deploy, and maintain production-grade AI/ML applications (including LLMs and agentic systems). Implement APIs, services, data pipelines, testing, monitoring, and CI/CD. Collaborate with data scientists, platform, security, and product teams to deliver secure, scalable, observable AI solutions that create measurable business value.

Lead global go-to-market and co-sell strategies with AI-native ISVs and foundational model providers. Drive joint-selling, co-marketing, pipeline management, sales enablement, and cross-functional coordination with NVIDIA product and account teams to create repeatable partnership GTM motions and measurable revenue outcomes.

Develop, deploy, and operate agentic AI systems that drive manufacturing decisions across S&OP, material planning, production, quality, logistics, and customer deployments. Embed with cross-functional teams to model demand, supply, BOMs, capacity, and risk; build agents that generate executable plans, propagate design changes, run historical replays and live evals, and continuously evaluate frontier models and optimization methods to improve ramp speed and schedule attainment.

Design and evolve enterprise-scale automation platforms across UI, API, integration, resiliency, and cloud-native validation. Implement AI-assisted test generation, autonomous maintenance, flaky-test analysis, and defect prediction. Integrate testing into CI/CD and cloud environments using containers and orchestration. Define architecture standards, build observability-driven testing, mentor teams, and collaborate on organization-wide quality engineering strategy and documentation.