Artificial Intelligence Jobs in San Jose, CA

Lead presales solutioning for Data and AI opportunities using Snowflake, design architectures, conduct workshops, and drive AI-infused data solutions.

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.

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

Own AI Cloud strategy and roadmap to showcase hardware advantages, define served models and pricing/tokenomics, write technical PRDs, drive cloud features for enterprise inference, incubate solution architectures, and align product with sales and partner motions to accelerate deals.

The Senior Applied AI Engineer will build AI-driven interactive product demos, collaborating with design and engineering teams to create intelligent multimodal experiences.

Lead AI software resiliency development, implementing features for reliability and fault tolerance in large-scale AI supercomputers, requiring hands-on coding and collaboration.

Lead technical engagement for AI training and infrastructure at scale. Collaborate with engineering teams and customers to ensure successful deployment and optimization of AI/ML solutions.

As an AI Algorithm Developer, you'll design and implement machine learning solutions for semiconductor processing, focusing on predictive models and data analysis.

Fine-tune and optimize LLMs (including multi-modal) for humanoid robots and autonomous vehicles; build scalable training and deployment pipelines; work on model efficiency (quantization, weight sharing), synthetic data generation, and cross-team product alignment.

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 architecture and build AI-native developer tooling and autonomous agent systems to accelerate engineering workflows. Drive platform standards for reliability, safety, observability, and cost-efficiency while partnering across teams and mentoring senior engineers.

Lead GPU and NVLink-based cluster design and validation for large-scale AI and HPC deployments. Advise cloud partners on architectures, perform performance modeling, debug deployment issues, support NPI rollouts, and relay field feedback to engineering.

Architect and implement low-latency sensor-to-GPU data pipelines, develop system software, drivers, and APIs, work across FPGA, networking, and CUDA stacks, integrate diverse sensors into Holoscan, and debug and optimize real-time distributed systems under strict latency and throughput constraints while collaborating with hardware, firmware, and AI teams.

Lead global product strategy and lifecycle for large-scale network orchestration, telemetry, and analytics products. Define technical use cases and architecture requirements, guide cross-functional teams, engage customers and partners, and deliver scalable datacenter networking solutions aligned with NVIDIAs AI infrastructure roadmap.

Lead a hands-on engineering team to ship production-ready LLM inference via NVIDIA Inference Microservices. Own model onboarding, serving integration, performance optimization, release quality, security readiness, automation, observability, and operational health while partnering with product, research, security, and ops.

Develop, optimize, and ship video processing, generative video, and 3D reconstruction algorithms (e.g., NeRF, 3D Gaussian Splatting) across desktop and mobile. Integrate and deploy deep learning models, run research and evaluations, build production-ready modules, and create test environments and tools for runtime verification of video and 3D pipelines.

Develop and enhance visuomotor manipulation policies for humanoid robots, focusing on real-world deployment and integration with other systems.

Lead strategy and development of AI and technology products, managing cross-functional teams to enhance legal workflows and compliance standards.

Lead development of AI agent frameworks for Windows, optimizing performance, ensuring security, and collaborating with AI research teams. Mentor engineers and establish best practices.

The role involves assisting customers with AI solutions, collaborating with teams, analyzing deep learning performance, and leveraging NVIDIA technology.

Intern will design and optimize software to deploy large-scale AI models on production vehicles, profile and improve inference across compute, memory, and I/O, reduce latency and power use on embedded automotive platforms, and deliver production-ready, fleet-scalable performance optimizations.

Lead global commercial strategy and execution for AI infrastructure sales across data center, edge, and industrial markets. Own revenue targets, build strategic partnerships with hyperscalers/OEMs, lead complex deals, scale a global sales organization, and align cross-functional teams on product roadmaps, supply chain, and market expansion.

Lead strategic positioning and integration of AI-driven and digital platforms for semiconductor process development. Define end-to-end solutions, orchestrate insight integration, partner with product and engineering, identify gaps and high-value AI use cases, and drive adoption and enablement across internal teams and customers.

Design, build, and operate scalable ML pipelines and infrastructure across cloud and on‑prem HPC. Implement MLOps tooling (tracking, registries, feature stores), CI/CD/CT, containerized GPU orchestration, model optimization (LLMs, GNNs, RL), data/versioning pipelines, monitoring, and mentor engineers to productionize ML for EDA and simulation workloads.

Design, build, and deploy LLM-powered tools, RAG-based knowledge systems, and agentic workflows to automate SOC integration tasks. Own reliability, evaluation, and observability for AI systems and collaborate with SOCD teams to scope, validate, and measure productivity gains from deployed automation.