Artificial Intelligence Jobs in San Jose, CA

Design and build native Android applications for robot data annotation, dataset review, and experiment tracking. Create fast, reliable mobile experiences for real-time data capture, integrate AI models to automate annotation, and collaborate with AI researchers, robot operators, and web tooling teams to ensure consistent workflows.

The Data Infrastructure Engineer will design, build, and maintain data tools for robot data management, supporting AI researchers and ensuring optimal data solutions across storage and compute resources.

The Director of AI will lead AI and data engineering teams, define and execute AI strategy, and ensure effective partnership across functions, focusing on scalable AI solutions and responsible AI practices.

Design and build scalable EDA flows and AI/LLM-driven automation for 3DIC, interposer, and RDL systems. Implement flow-generation, debugging assistants, root-cause analysis, and platform integration using C++, Tcl, Python, and Java. Collaborate with engineering teams to validate flows, automate constraint-driven designs, and address large-scale, high bump-count and multi-region routing challenges to deliver production-quality solutions.

The Physical Design Engineer will lead efforts in solving physical design challenges, improve RTL-to-GDS methodologies, and integrate AI/ML solutions into CAD flows, focusing on performance and runtime improvements.

Lead AI feature design and delivery, build large-scale ML systems, mentor engineers, and define best practices across the engineering organization.

Design, build, and optimize distributed PyTorch training systems for multi-node GPU clusters. Profile and eliminate performance bottlenecks across data pipelines to GPU kernels, implement low-level CUDA/cuDNN/Triton kernels, tune CPU/GPU/memory/network utilization, and develop monitoring and debugging tools for large-scale training runs.

Design, build, and operate agentic AI workflows and MCP servers for an LLM-based network operations assistant. Implement LangGraph orchestration, RAG/memory systems, evaluation frameworks, and production integrations (Kubernetes, CI/CD, containers, FastAPI). Collaborate with domain experts, ensure observability, and drive reliability of non-deterministic AI systems at scale.

Work as an early-career full-stack software engineer building and maintaining web application features across backend, frontend, APIs, data, tests, and internal tooling. Use AI-assisted development tools responsibly, write maintainable code and tests, participate in code reviews, and learn product and customer context to deliver production-quality outcomes.

Build, train, evaluate, and deploy production computer vision models (detection, segmentation, OCR, barcode decoding, visual search). Own dataset quality, tooling, model releases, inference exports, and monitoring. Collaborate cross-functionally to improve model performance, robustness, and deployment for retail robotics and edge environments.

Own the full product loop: identify high-impact problems, rapidly prototype and ship AI-accelerated solutions, build internal tools and automations, collaborate with operations and business teams, and iterate based on usage and data to drive adoption and business impact.

As a Senior Full-Stack Software Engineer, you will design, build, and ship AI-powered systems to optimize workflows in the single family rental market, focusing on backend services and full-stack development in a fast-paced environment.

The Founding AI Engineer will design and build an AI-native compliance platform, handling complex financial data and developing scalable backend services, while establishing processes for enterprise readiness.

The intern will design and implement AI-driven automation workflows to enhance the silicon testing process, collaborate with engineers, and document automation recipes.

Lead and inspire a small team in AI research focused on autonomy and knowledge capture related to semiconductor equipment. Responsible for project roadmap and development of software modules.

Lead the development and management of AI infrastructure, ensuring optimal performance of systems and providing mentorship to engineers while deploying advanced AI models.

Lead product strategy and roadmaps for inference tools, SDKs, and libraries that enable developers to deploy and optimize GenAI models on NVIDIA GPUs. Partner with internal and external developers, engineering, leadership, and marketing to prioritize features, drive product delivery, and define go-to-market plans focused on performance, cost, and safety.

Lead an enterprise AI product pod to define use cases, KPIs, scope, and adoption; coordinate cross-functional stakeholders (Legal, Quality, IT/Security); ship LLM/agent/RAG solutions into real workflows; produce reusable Fabric assets; manage roadmap, telemetry-driven adoption, and portfolio reporting.

Design and validate high-bandwidth interconnects for AI clusters across copper and optical media. Define architectures, perform link and channel budget analysis, lead qualification and reliability testing, develop validation plans for transceivers and cables up to 1.6T, and coordinate with suppliers and manufacturing.

Own strategy and execution for a major compliance product area: set multi-year vision, lead cross-functional initiatives, use customer and data insights to prioritize, partner with engineering on model-driven systems, mentor PMs, and engage enterprise customers for demos and requirements.

Build and prototype state-of-the-art large language models and AI assistants, take services from ideation to production, fine-tune open-source models, use LLM platforms, analyze model statistics and projections, and collaborate cross-functionally in a fast-paced hybrid Palo Alto environment.

Lead developer relations for NVIDIA's local AI ecosystem: manage strategic partner portfolio, provide technical guidance, drive adoption of NVIDIA accelerated computing and runtimes, define joint plans and success metrics, enable partners with integrations and content, represent partner needs internally, and evangelize local AI at events.

Lead technical discovery and demos for enterprise prospects, translate pain points into LLM-powered solutions, present architecture and value, and feed customer insights to Product, Marketing, and R&D to shape the platform and sales strategy.

Serve as a technical advisor to research labs to accelerate adoption of NVIDIA AI and computing platforms. Map research ecosystems, track emerging scientific AI trends, collaborate cross-functionally, enable researchers through demos, workshops, and technical content, and represent researcher needs to inform product, education, and platform roadmaps. Attend conferences and foster long-term academic and national-lab partnerships.

Build and productionize AI applications integrating LLMs and vision models into an edge-to-cloud robotics platform. Fine-tune and quantize models, deploy to NVIDIA Orin/Jetson and cloud GPUs, integrate Vector DBs/RAG/agent frameworks, and create data pipelines to collect and version sensor data for retraining.