Artificial Intelligence Jobs in San Jose, CA

Quota-carrying strategic account manager responsible for driving multi-year datacenter storage adoption across platform and infrastructure customers. Own account strategy, pipeline, and complex deals; lead architecture-led selling, executive engagement, product qualification, and new technology introduction to convert roadmap alignment into repeatable revenue.

Lead a multidisciplinary team building Cisco's CIRCUIT AI platform: define agentic architectures, benchmark and fine-tune models, optimize model serving and inference efficiency, create evaluation harnesses and metrics, set enterprise-grade security and governance, and drive platform strategy and measurable AI productivity and reliability improvements.

Lead research and development of novel ML and deep learning algorithms from data and architecture design through evaluation and transition to production. Prototype research ideas, run benchmarking experiments, build reproducible research tooling, and communicate findings to leadership.

The role involves building AI tools and coding agents, fine-tuning LLMs, and collaborating on product features to support CUDA developers at NVIDIA.

This role focuses on driving adoption and alignment for ODM partners, supporting data center delivery, and incorporating partner feedback into product strategy.

The Senior Staff AI Engineer will lead the design and optimization of reinforcement learning systems for enterprise applications, focusing on scalable architectures and production deployment. Responsibilities include mentoring, collaboration, and ensuring compliance with AI ethics and safety standards.

As an AI/ML Engineer, you will design and deploy models for real-world applications, optimize data pipelines, and engage with global leaders to enhance AI technology's impact across sectors like healthcare and government.

As a Senior AI Engineer, you will architect AI systems for marketing, build generative pipelines, and ensure compliance with privacy standards, all while leading MLOps processes.

Lead the go-to-market strategy for an AI-powered product, oversee product releases, collaborate with teams, and drive market positioning.

The Senior Software Engineer will work on optimizing open-source LLM inference, enhancing performance on NVIDIA GPUs, and collaborating on production-ready solutions.

As a senior software engineer at NVIDIA, you will design and build tools for performance analysis of AI workloads, lead automation efforts, and enhance workflows for engineers.

Apply machine learning to real-time graphics and interactive experiences: develop models from latest research, prototype applications, collaborate across teams, and productize neural graphics features for gaming and visualization.

Architect and demonstrate simulation workflows using DSX Sim and Omniverse libraries to help partners and customers build, validate, optimize, and operationalize AI factory digital twins. Drive integration of Omniverse libraries, develop reference architectures, prototypes, benchmarks, and sample workflows, and coordinate with sales, product engineering, and partners to resolve blockers and advance adoption.

Advise ISVs on designing, deploying, and optimizing large-scale accelerated AI infrastructure. Lead architecture reviews, POCs, benchmarks, and production deployment guidance across compute, networking, storage, containers, orchestration, observability, and CI/CD. Create reference architectures, sizing guidance, technical playbooks, demos, and whitepapers. Support cluster monitoring, reliability, and performance improvements. Up to 20% travel for customer engagements.

Lead partner engagements to design, prototype, and deploy production-grade agentic AI systems on NVIDIA GPUs. Architect multi-agent workflows, RAG, tool use, planning, memory, evaluation, and guardrails; build PoCs, benchmarks, and reference architectures; optimize performance and cost; guide model customization and post-training workflows; use agent harnesses and translate findings to product and engineering teams for platform improvement and field enablement.

Develop and maintain AI networking communication frameworks, production benchmarking tools, and automation for large-scale AI training/inference. Enable models in benchmarking infrastructure, analyze end-to-end performance, perform large-scale parameter searches, and collaborate with networking and hardware teams to co-design features and optimize system performance.

Develop and optimize end-to-end data pipelines for autonomous driving, ensure data quality, and support model training through efficient data management processes.

Lead the AI strategy and programs for the PSM organization, focusing on AI-driven initiatives, tools integration, and stakeholder alignment to enhance productivity and business outcomes.

Design and implement high-performance graphics systems and pipelines for 3D generative AI products. Optimize rendering kernels and geometry processing on GPUs, integrate production-quality pipelines with data and model infrastructure, ensure scalability across GPU clusters and cloud, and collaborate with artists, ML researchers, and product teams. Contribute tooling, documentation, demos, and open-source work to advance Meshy's graphics capabilities.

Build and optimize end-to-end 3D ML systems: training frameworks, high-throughput distributed training across large GPU clusters, custom Triton/CUDA operators, data-loading libraries, and efficient multi-stage inference pipelines with model optimization (compilation, fusion, quantization). Collaborate with researchers on model architecture and maintain production inference endpoints.

Design, build, and operate LLM-facing backend services and evaluation systems. Develop prompts, context pipelines, tool-calling workflows, and retrieval systems. Measure and improve model grounding, consistency, latency, and cost. Ship production-ready solutions and rigorous regression/evaluation pipelines for real-world therapeutic data.

Lead co-design and build of production agentic and Generative AI systems with strategic ISV partners. Define objectives, prototype and ship RAG, multi-agent, long-horizon workflows on NVIDIA's stack, drive adoption, collaborate with Product/Engineering/Research, and influence product roadmaps. Ensure production inference, evaluation, observability, and enterprise deployment (MLOps/Kubernetes/Docker) with strong technical execution and partner-facing leadership.

Design, develop, and maintain generative AI and cloud-based AI solutions using Microsoft Azure and Power Platform tools. Collaborate with cross-functional teams to gather requirements, build scalable AI applications, deploy and manage models with Azure Machine Learning, test/debug, document designs, and provide training and user support.

Research and implement methods to transfer human-object interaction data into robotic dexterous manipulation policies. Build large-scale data pipelines, generative embodied-AI models, unified cross-embodiment training, and sim-to-real deployment; validate on real multi-fingered or humanoid hardware and produce a technical manuscript or demo.

The Technical Product Manager will lead product direction, interface with engineering teams, and ensure the successful deployment of AI-powered safety systems. Responsibilities include defining product vision, translating customer needs, and maintaining high product quality.