Artificial Intelligence Jobs in San Jose, CA

The role involves developing AI-driven compiler technologies, optimizing workflows, integrating learning systems, and collaborating on next-gen compiler capabilities for improved performance.

Lead product strategy, roadmap, and go-to-market for NVIDIA enterprise game development solutions. Partner with engineering, ISVs, OEMs, and sales to integrate RTX, generative AI, and developer workflows into studio pipelines. Drive positioning, demos, trade-show strategy, sales enablement, and lighthouse case studies to grow adoption among game developers and publishers.

Build scalable, resilient agentic AI and LLM/ML solutions. Translate requirements into high-level and detailed architectures. Implement solutions using Python/C++ and ML frameworks, applying AI/ML fundamentals, data structures, algorithms, and distributed systems.

Design behavioral specs, harnesses, and validation systems so AI agents implement reliable full-stack features. Build CI gates, structural tests, linting, knowledge bases, and end-to-end verification. Diagnose failures across spec, harness, and agent layers. Architect and operate production systems across cloud and on-prem environments for regulated energy deployments, prioritizing reliability, observability, and graceful degradation.

As a Senior AI Systems Performance Engineer, you will benchmark AI workloads, optimize Ethernet fabric performance, and troubleshoot system bottlenecks, focusing on delivering actionable insights across teams.

Lead technical and operational programs for enterprise AI partners. Define schedules, translate customer requirements, drive cross-functional execution from design to production, manage risks and post-deployment support, and coordinate global teams to integrate complex AI model stacks into customer environments.

Lead and grow a technical team responsible for verifying AI compilers for next-generation workloads. Define formal verification requirements, drive AI-assisted and compiler-aware verification (fuzzing, differential testing, symbolic reasoning, formal methods), and partner with compiler, CUDA, runtime, and ML framework teams to build scalable verification infrastructure and improve production confidence.

Architect and optimize large-scale GPU AI infrastructure: profile distributed training/inference, diagnose networking and system bottlenecks, design high-performance clusters, lead POCs, automate benchmarking, and drive technical engagements with customers and internal teams.

The internship involves multiscale modeling focusing on physics and AI modeling. Interns will run simulations and define workflows using Python and related libraries.

Design and implement onboard perception systems (computer vision, localization, visual-motor policies, and data annotation), own end-to-end product lifecycle, deliver production-grade software, and collaborate cross-functionally to optimize the autonomy stack for humanoid robots.

The role involves owning the infrastructure for training and deploying RL-based control systems in humanoid robots, enhancing speed, efficiency, and collaboration among teams.

Design, build, and operate agentic AI and copilot systems for Roku TV: prototype, architect, and deploy end-to-end agents including model selection, RAG/context pipelines, tool-calling, orchestration, observability, testing, and continuous improvement to ensure reliable production performance.

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.

Lead software-hardware co-design to optimize LLM and agentic AI inference. Profile and optimize model execution for memory bottlenecks, influence memory and accelerator architecture, and develop/benchmark inference and serving solutions using PyTorch and vLLM while mentoring teams on best practices.

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.

Lead development and optimization of local AI agent frameworks and runtimes on Windows and GeForce RTX hardware. Ensure secure, policy-driven sandboxing, privacy routing, and efficient local LLM inference. Collaborate with AI research, driver teams, and open-source communities while mentoring engineers and delivering production-ready integrations for consumer PCs.

Lead design and delivery of a scalable data and AI platform for supply chain operations. Architect and build Databricks/AWS-based ETL/ELT, streaming, CDC, RAG and agentic AI solutions; implement data models, quality frameworks, observability, and production reliability; manage and mentor a high-performing engineering team while partnering with product and business stakeholders to deliver production-ready systems.

Design, build, and deploy AI agentic systems and RAG applications that integrate LLMs with enterprise data and tools. Enable company-wide adoption of Copilot and Claude, deliver conversational agents, ensure performance via testing and monitoring, collaborate with stakeholders, and mentor teammates to drive measurable business value.

Lead development of autonomous agentic AI tools for threat research and security use cases. Integrate external/internal feeds, build AI-driven security and technical marketing tools, create pipelines for ingestion, observability, testing, and deployment, and deliver dashboards, reports, demos, and reusable assets while collaborating cross-functionally.

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.

Lead design and implementation of cloud-native, microservices backend systems that power AI-enabled features (LLM assistants, RAG pipelines, recommendations). Build scalable REST and event-driven APIs, establish MLOps practices, ensure reliability/security/observability, mentor engineers, and collaborate with product, scientific, and infrastructure teams to productionize AI services in regulated scientific environments.

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

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.

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.

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