Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Deploys and validates complete AI infrastructure software stacks on multi-node GPU systems, then converts implementations into technical documentation, automation, demos, training, and reference architectures. Tests prerelease software, evaluates interoperability and operational resilience, supports partners and field teams, collaborates with engineering and open-source communities, and recommends product improvements based on customer feedback. The role presents solutions through briefings, workshops, webinars, events, and internal training, with some travel required.

Lead NVIDIA’s RL post-training frameworks strategy and engineering ecosystem across distributed training, inference, rollout, evaluation, orchestration, and NVIDIA platforms. Build and manage globally distributed teams, prioritize upstream and internal investments, establish benchmarks and execution metrics, and drive reliable open-source integrations. Partner with research, product, hardware, CUDA, networking, and external communities to scale reinforcement learning workloads across GPUs and heterogeneous systems.

Develop HPC and AI software workflows for experimental data processing, imaging, plasma, high-energy physics, and materials simulations. Profile applications, benchmark performance, identify bottlenecks, and advise collaborators and product teams. Build tutorial examples, translate research into software tools, and represent NVIDIA at scientific conferences and industry events.

Lead the technical narrative and content strategy for NVIDIA DSX, an AI factory platform spanning facilities, power and cooling, compute, networking, storage, software, orchestration, simulation, models, and workloads. Develop reference architectures, whitepapers, guides, demos, workshops, training, videos, and launch assets. Coordinate cross-functional teams and partners, lead flagship demos, identify ecosystem opportunities, influence roadmap and positioning, mentor contributors, and measure content adoption.

Drive the development and roadmap of HPC and AI/ML products for physics, plasma, high-energy-density simulations, and resilient materials discovery. Define features, requirements, positioning, and go-to-market strategy; collaborate with engineering and NVIDIA software teams; identify customers and partnerships; and represent products at workshops and conferences.

Lead the architecture, prototyping, deployment, and scaling of agentic AI platforms for NVIDIA Marketing. Translate marketing opportunities into reliable AI agents, reusable platform capabilities, and production architectures. Establish practices for evaluation, observability, security, governance, reliability, and cost optimization while partnering with engineers on integrations, tooling, retrieval, memory, orchestration, and infrastructure. Guide complex initiatives from discovery through ongoing operations and communicate technical solutions to technical and business stakeholders.

Lead architecture and delivery of an ML infrastructure efficiency platform across silicon co-design teams. Define platform contracts, onboarding, orchestration, auth, observability, storage/caching, and SLA enforcement. Own production operation, security, reliability, and performance while mentoring engineers and driving cross-team technical decisions.

Lead HBM and LPDDR system integration and bringup for next-generation SoCs. Own memory performance, power, thermal, and reliability closure across silicon, package, board, and firmware. Drive post-silicon margining, VF shmoo, eye characterization, and correlation. Debug training, calibration, SI/PI, and system stability issues. Define validation, characterization, and issue-tracking plans to produce production-ready operating points.

The role involves owning physics models, defining manufacturing specifications, and validating designs to ensure product quality and performance. The architect will utilize first-principles thinking and AI to improve workflows and drive alignment across engineering teams.

Design and optimize custom SRAM circuits for performance, area, power, functionality, and robustness across advanced semiconductor processes. Collaborate with mask designers on dense pitch-matched layouts, verify electrical integrity, and improve design automation, data collection, and analysis flows. The role supports custom memory development for NVIDIA processing chips and requires strong SRAM design expertise, circuit design experience, and familiarity with semiconductor design and verification tools.