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

Lead strategic GTM and co-selling initiatives with Google Cloud to drive NVIDIA AI adoption. Develop and maintain technical enablement assets, run developer activations and events, collaborate with Google Cloud field and engineering teams, convert samples into demos and labs, and translate customer feedback into product and roadmap insights.

Design and implement production-grade VLSI/EDA tools and methodologies for multi-die package design. Build flows, applications, and automation to construct, analyze, validate, and optimize package designs to accelerate tape-out and scale engineering teams.

Analyze and optimize HPC application performance across CPU and multi-node multi-GPU systems by driving compiler and library improvements. Port and tune applications, evaluate compiler/runtime/driver impacts, and collaborate with compiler development and application engineering teams to deliver performance enhancements.

Build and maintain automation flows and methodologies for VLSI interposer design. Develop tools and applications (Python/C++) to construct, analyze, and validate interposer designs, improve designer workflows, and integrate AI/ML-assisted techniques into EDA environments.

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.

Lead founding AI Safety & Security Engineering team, define harness architecture and engineering standards, drive Find/Validate/Patch roadmap, mentor engineers, and translate research into production-grade, secure ML systems.

Develop and advance multimodal agentic AI systems using deep learning, reinforcement learning, large language models, and audio-visual reasoning. Responsibilities include training and deploying foundation models, improving post-training and alignment methods, developing planning and tool-use capabilities, managing multimodal datasets and evaluation pipelines, and leading research on embodied AI, voice interaction, and long-horizon task completion.

Develop and formalize methods to validate reported vulnerabilities and generate/verify safe patches for internal NVIDIA targets. Define correctness, regression, and revalidation standards; produce repeatable, auditable processes and evidence. Apply AI-assisted workflows cautiously, perform bounded applied research on vulnerability classes, and document findings for independent security review.

Design, develop, and optimize system software and firmware for SoC-based O-RAN infrastructure. Implement power-management policies (DVFS, clock/power gating, thermal control), analyze RU/DU workloads, build performance and power models, create measurement and test tooling, validate on simulation/emulation and silicon, and influence SoC and platform architectures for 5G/6G deployments.

Lead and coach a storage production engineering team to design, deploy, and operate large-scale storage systems. Own capacity planning, lifecycle management, HA/disaster recovery, automation, monitoring, incident response, and partnerships with engineering and AI teams to improve data pipelines and performance.