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

Lead hardware-software co-design for LLM inference: drive and document architecture, build executable C++ hardware models, optimize performance with good algorithms and parallelism, diagnose cross-team chip and subsystem issues, and automate workflows to improve efficiency.

Design and implement compiler optimizations and infrastructure for deep learning inference. Analyze networks, improve performance and memory, define public APIs, collaborate with framework and hardware teams, and deliver ahead-of-time and just-in-time compilation solutions.

Design, build, deploy, and operate large-scale GPU cloud infrastructure and tooling for AI training and inference. Perform performance analysis, capacity management, monitoring, automation, incident response, and lifecycle support for distributed multi-GPU/multi-node systems.

Lead full-flow physical implementation for LPU ASICs: synthesis, floorplanning, place & route, timing closure, UPF/LEC low-power flows, and tapeout. Collaborate with IP, front-end, PnR and sign-off teams to optimize PPA and resolve bottlenecks. Architect automated, data-driven EDA methodologies, implement scripts and AI-assisted optimizations, and ensure sign-off quality including power grid, EMIR, and DFT integration for high-speed SerDes blocks.

Lead Infrastructure Capacity Management programs, drive capacity operations and procurements, enhance EDA Farm efficiency, and establish communication across teams.

The Senior Software Engineer will work on optimizing open-source LLM inference, enhancing performance on NVIDIA GPUs, and collaborating on production-ready solutions.

The role involves collaborating with customers to optimize AI workloads, leading proof-of-concepts, and developing software for NVIDIA's AI frameworks while ensuring clear communication and team collaboration.

The role involves physical layout design for high-speed mixed-signal circuits, collaborating with cross-functional teams, and utilizing various design and verification tools.

Research and develop techniques to accelerate large-scale applications on advanced CPU platforms. Analyze and optimize database and analytics workloads for CPU performance, collaborate with internal and external experts, publish findings, and influence next-generation hardware/software design.

Develop end-to-end perception solutions for autonomous driving, leveraging deep learning and data-driven approaches to enhance system accuracy and efficiency.