Artificial Intelligence Jobs in Santa Clara, CA

Lead and execute high-impact AI initiatives, serving as a technical authority on agentic systems and fullstack development, while mentoring and optimizing infrastructure strategy.

The Senior Staff Physical AI Data Algorithm Engineer is responsible for designing and optimizing a vehicle-cloud data closed-loop architecture, managing data toolchains, and ensuring high-quality data standards throughout the model development cycle.

Lead Finance AI transformation for FP&A and Treasury: identify and pilot high-value AI/automation use cases, build prototypes, define governance and controls, design finance data products and dashboards, drive adoption, and track measurable value realization across planning, forecasting, cash visibility, and controls.

Design, build, and tune hybrid retrieval and RAG systems combining Elasticsearch, vector/embedding search, and re-ranking models. Improve relevance using query analytics, behavioral signals, experimentation, and evaluation metrics to enhance search quality and Generative AI responses. Collaborate with AI engineers, platform teams, and product stakeholders.

Lead PR for NVIDIA AI software: craft narratives, pitch media, build relationships with AI software teams and reporters, implement cross-functional PR strategies, and secure impactful coverage across industries.

Support proactive PR storytelling about AI and industries (healthcare, sustainability, telecom, finance, media, energy, retail). Build and maintain media relationships, manage logistics for announcements, interviews, and events; coordinate partner review/approval; write press releases and company announcements; track and analyze media coverage; assist on special projects to promote NVIDIA's AI leadership.

Work directly with enterprise customers to design, build, and deploy production voice AI applications. Integrate Retell APIs/SDKs, develop conversational workflows, debug telephony/SIP and infrastructure issues, optimize AI behavior and prompts, lead technical onboarding, and drive product improvements from proof-of-concept to production.

Lead architecture and development of next-generation scale-up fabrics and collaborative communication for AI data-centers. Ensure scalability, reliability, and robust large-model training across hardware, software, and applications while driving innovation and cross-functional implementation toward production and manufacturing.

Lead design and implementation of agentic code-generation systems, scale distributed high-performance production infrastructure, develop AI evaluation frameworks and benchmarks, optimize agent outputs for NVIDIA GPUs, and drive engineering standards for production-grade agentic AI.

Lead and transform the US pitch and pursuit process by embedding AI enablement across stages. Manage a searchable global pitch content library, maintain structured experience and CRM data, create reusable pursuit assets and prompt frameworks, and develop training and adoption materials. Partner with BD, lawyers, and global teams to improve workflow, support high-priority pitches, and oversee directory and awards submissions to increase speed, quality, and consistency of wins.

The role involves defining product vision and strategy for in-vehicle AI experiences, leading product execution, integrating AI, and partnering with engineering and automotive customers for successful deployment.

Lead end-to-end evaluation and benchmarking of NVIDIA's Generative AI (Nemotron) models for strategic ISV partners. Design evaluation frameworks, experiments, and metrics; translate results into go/no-go recommendations; deliver dashboards and executive readouts; partner with research, product, and engineering to set release criteria and drive model-improvement signals and roadmap priorities.

Lead and grow a team of software product managers for HPC/AI solution software. Own people leadership, drive consistent product practices, align roadmaps with architects and engineering, ensure delivery discipline, mentor talent, and shape portfolio-level strategy and execution.

Design and build scalable backend services and APIs for HPE EdgeConnect Orchestrator, integrate AI/LLM-driven workflows and Copilot capabilities, work with analytics and telemetry for real-time insights, and ensure quality through testing, debugging, and performance optimization.

Lead and conduct original research in embodied AI for autonomous systems, developing and evaluating large-scale perception, decision-making, and control models. Translate research into deployed prototypes, collaborate with engineering and field teams, support field tests and data collection, and define research strategy across reinforcement learning, vision-language-action, sim-to-real, memory, and multi-agent coordination.

Develop and deploy multimodal foundation models and agentic AI for multi-robot coordination. Design training recipes, curate datasets, build simulation and RL environments, evaluate performance, and integrate models into real-time robotic systems with field trials.

Partner with HR COEs to identify and prioritize AI use cases, evaluate and optimize Workday and AiMEE capabilities, lead pilot deployments, collaborate with data science and GIS teams to build HR analytics and AI solutions, and support planning, testing, and deployment to improve employee experience and process automation.

Research, design, and train foundation models for robotics; align text-based reasoning with low-level control policies; evaluate zero-shot generalization; run experiments; integrate models into robotic systems and present findings externally.

Design and architect multi-agent orchestration frameworks, build tool-calling pipelines and reasoning loops, and optimize latency and token usage for real-time agentic workflows and autonomous operations.

Develop multimodal intent-recognition models from gaze, gesture, and voice; implement socially aware navigation respecting personal space; design robot visual and auditory UI feedback; run user studies and rapidly prototype AI-driven voice and visual interfaces.

Run adversarial tests on language and multimodal models, build guardrails and real-time filters for autonomous tool use, and support RLHF alignment and constitutional AI development to ensure safe AI deployment.

Lead product strategy and roadmap for AI and robotics products, translating VLA and agentic RAG breakthroughs into customer value, defining safety/usability/performance metrics, working with enterprise adopters, and driving cross-functional delivery.

Build and operate MLOps and agentic infrastructure: manage model registries, continuous training loops, and A/B testing; deploy agents as scalable Kubernetes microservices; and create observability dashboards tracking token usage, latency, and agent reasoning.

Design, orchestrate, and optimize large-scale LLM pre-training across 1,000+ GPUs. Implement 3D parallelism, manage GPU clusters (SLURM/Kubernetes), optimize InfiniBand/RDMA networking and memory, and automate checkpointing and failure recovery for long training runs.

Design and integrate vision and audio models into agent reasoning loops, optimize streaming latency for voice-to-voice interactions, and architect multimodal RAG systems to retrieve insights from videos and PDFs.