Artificial Intelligence Jobs in Santa Clara, CA

Design, build, and operate end-to-end training and inference infrastructure for large language and multimodal models. Improve efficiency (memory, parallelism, kernel optimizations), ensure robust scalable training and RL pipelines, optimize low-latency/high-throughput serving (quantization, caching, speculative decoding), manage multi-GPU and multi-cloud orchestration, and productionize new algorithms with strong observability and reproducibility.

Lead enterprise AI, GenAI, agentic automation, MLOps, and RPA strategy and delivery. Build multi-cloud LLM platforms, enable model lifecycle management, deploy multi-agent systems, run an RPA COE, partner with security and legal, and drive production-scale AI programs with measurable business impact.

Design, deploy, and maintain large-scale AI training and inference infrastructure. Scale distributed training, optimize GPU/hardware utilization, manage multi-cloud compute and orchestration, maintain MLOps tooling (MLflow), build high-throughput data pipelines, and partner with researchers and software engineers to productionize and monitor LLMs and other AI models.

Build production-grade AI agent systems, automations, and workflows across recruiting, security/compliance, finance, onboarding, CRM integrations, and growth. Implement LLM-powered solutions, API integrations, and operational tooling to improve hiring, sales, and enterprise readiness.

The role involves applying diffusion techniques and developing AI models to enhance 3D reconstruction pipelines for various industries.

The Lead Infrastructure and Reliability Engineer will enhance GPU operations, define scalability strategies, and develop organizational strengths in a high-demand AI infrastructure setting.

Lead the development of AI/ML deployment infrastructure, collaborate with engineers, develop pipelines, manage teams, and enhance data applications.

Develop AI/ML models for 3D simulation and geology prediction, refine data pipelines, and integrate deep learning tech with product backend.

As an AI Platform Backend Engineer, you'll design and deploy backend systems utilizing AI for real-world applications, optimize performance, and mentor junior engineers while working directly with clients to address complex challenges.

As an AI Platform Engineer, you will design and develop AI-driven backend solutions, optimize performance, and collaborate with customers and teams.

Create developer-focused technical content, demos, reference examples, deployment guides, and docs-as-code workflows to accelerate adoption of NVIDIA enterprise AI software. Work across engineering, product, field, and partners to design developer journeys, build sample applications and notebooks, enable sales and partners, capture feedback, and engage the open source and cloud-native communities.

Serve as the embedded systems expert for NVIDIA Physical AI partners, integrating sensors, deploying and optimizing robotics foundation models on Jetson-class edge hardware, guiding customers from prototype to production, and delivering reference implementations and workshops.

Lead product strategy and execution for AI-driven Copilot, agentic AI, and workflow orchestration on the Pure1 platform. Define roadmap, drive integrations, expand SaaS subscriptions, and partner with engineering, GTM, and customers to deliver automation and AI-powered infrastructure services.

This role involves designing and exporting AI frameworks, enhancing product capabilities, collaborating with teams, and directly engaging with customers.

The Principal AI Compiler Engineer will design and enhance an AI compiler toolchain, integrating AI for optimizing neural network deployment on SoCs, partner with hardware teams, and improve performance through diagnostics and profiling.

Lead sales efforts for AI and Robotics, focusing on revenue growth and customer relationships. Drive top-line revenue through strategic sales motions.

As a Senior Staff Software Engineer, you will develop and maintain AI deployment software, optimizing hardware-software co-design, and work closely with various experts to enhance infrastructure.

The Senior Technical Program Manager is responsible for site and data center operations, leading cross-functional programs, ensuring operational readiness, and managing metrics and KPIs for AI infrastructure.

Lead GPU and HPC cluster architecture and deployment for cloud partners: design end-to-end GPU/NVLink infrastructures, perform performance modeling, validate and debug cluster configurations, support NPI deployments, and serve as technical liaison between engineering, product, and field teams.

Own global channel strategy and execution for NVIDIA Jetson and IGX platforms. Manage distributor relationships, drive partner onboarding, pipeline creation, revenue, and partner readiness. Collaborate with marketing on channel programs, MDF-funded activities, demand generation, and performance measurement through reviews, scorecards, and dashboards.

Design, implement, and operate a private, secure AI platform and autonomous agent workforce for engineering workflows. Build agents (FastAPI/CLI/chat), integrate MCP and RAG, maintain containerized infrastructure, CI/CD validation, secrets and cost management, and enterprise integrations (GitHub, Jira, Confluence, Teams). Continuously evaluate models, improve agent reliability, and drive platform adoption across low-level systems engineering domains.

The role involves integrating AI supercomputing systems into datacenters, collaborating with engineering teams, and producing documentation for processes related to datacenter integration and fit-out.

Create developer-facing technical content for NVIDIA's AI platform: test and evaluate training/inference features, write blogs and guides, build code samples and demos, benchmark multi-GPU systems, engage the developer community, and feed product feedback to engineering and product teams.

Design, implement, and optimize AI algorithms and software for computer vision, NLP, recommendation systems and ASR. Develop, test, and maintain models and production-quality code, participate in code reviews and CI/CD workflows, and document algorithms and technical processes.

Design, build, and deploy reliable, outcome-driven AI systems and agentic workflows that plan, reason, and act across enterprise systems. Integrate LLMs into production, work with structured and unstructured data to ground models in business context, and partner with product to ship end-to-end capabilities.