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Required RTL Design Technical Lead, Servers, Cloud
About the job
Be part of a team that pushes boundaries, developing custom silicon solutions that power the future of our direct-to-consumer products. You'll contribute to the innovation behind products loved by millions worldwide. Your expertise will shape the next generation of hardware experiences, delivering unparalleled performance, efficiency, and integration.
As part of our Server Chip Design team, you will use the ASIC design experience to be part of a team that creates the SoC VLSI design cycle from start to finish. You will collaborate with design and verification engineers in active projects, creating architecture definitions with RTL coding, and running block level simulations.
The ML, Systems, & Cloud AI (MSCA) organization designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all our services (Search, YouTube, etc.) and Cloud. Our end users are Googlers, Cloud customers and the billions of people who use our services around the world.
We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Clouds Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.
Responsibilities
Lead the Design Activities at IPs, SubSystems(S.S) and SoC.
Plan, execute, track progress, assure quality, report status of the assigned activity.
Lead a team of designers both directly and in teams.
Define the Block/SoC level design documents such as Micro Architectural Specifications.
Own IP, S, SoC strategies for clocks, resets, and debugs. Enforce global methodologies and drive enhancements.

Required Senior SOC Performance Engineer, Cloud
About the job
Be part of a team that pushes boundaries, developing custom silicon solutions that power the future of our direct-to-consumer products. You'll contribute to the innovation behind products loved by millions worldwide. Your expertise will shape the next-generation of hardware experiences, delivering unparalleled performance, efficiency, and integration. our mission is to organize the world's information and make it universally accessible and useful. Our team combines the best of our AI, Software, and Hardware to create radically helpful experiences. We research, design, and develop new technologies and hardware to make computing faster, seamless, and more powerful. We aim to make people's lives better through technology.
Responsibilities
Validate performance and power models from the architecture team and lab measurements against established goals, exercise open source benchmarks, analyze the results, and identify optimization opportunities.
Design and build tests to verify SoC design meets targets, and implement advanced technologies for running "benchmark representations" on pre-silicon environments.
Analyze problems to identify core design weaknesses, and drive resolution of performance issues in both pre- and post-silicon environments.
Collaborate closely with design, validation, and architecture teams to ensure hardware and software designs interface correctly and deliver products.
Develop performance measurement frameworks, including Key Performance Indicators (KPIs), to produce regular reports and dashboards that support stakeholder decision-making, and drive efforts to productize features that improve performance and power characteristics.

we are looking for a Senior Machine Learning Engineer to work on LLM-based systems with a strong emphasis on integration, end-to-end workflows, and production readiness. This role combines performance optimization (prefill/decode, throughput, latency) with hands-on work integrating components across the AI platform.
A significant part of the role involves building end-to-end integration flows and tests, particularly around token generation pipelines and system orchestration, as well as contributing to intelligent system behavior such as hardware selection and execution strategies. The role also includes developing system-level logic in Python for multi-tenant management, caching strategies, and service lifecycle management across the platform.
***This is not a Data Science position***
Responsibilities:
Build and maintain end-to-end integration flows across the AI inference pipeline (serving, orchestration, APIs, and infrastructure)
Design, implement, and optimize LLM inference workflows, including prefill and decode stages
Improve system performance with focus on throughput, latency, and interactivity
Write production-grade components in Python and integrate them into the broader system
Contribute to system-level logic such as smart hardware selection and execution strategies
Integrate models (open source and custom), services, and APIs into cohesive, reliable end-to-end application pipelines

we are seeking a Lead System Architect to join our system architecture team and help define the next generation of our AI-SuperNIC scale-out chip.
AI scale-out communication is a critical element in modern data centers, and emerging standards such as Ultra Ethernet aim to address this challenge. This role focuses on defining a high-performance Smart NIC architecture optimized for GPU-centric AI workloads, with emphasis on low-latency, high-bandwidth data movement.
You will work across hardware and software domains, collaborating closely with AI, platform, driver, and VLSI teams to design a competitive scale-out networking solution.
Responsibilities:
Define product requirements and architecture for next-generation ultra low-latency AI-SuperNIC
Design end-to-end data paths across NIC, host, and GPU memory, with focus on GPU connectivity (PCIe, NVLink, CXL)
Lead adoption of emerging standards such as UEC and UALink, while maintaining compatibility with existing ecosystems
Establish architecture guidelines and partitioning across hardware, firmware, and software
Drive performance modeling, analysis, and system-level optimization for AI workloads
Optimize scale-out communication patterns (e.g., collectives) for GenAI training and inference
Collaborate with software teams on drivers, runtimes, and integration with AI frameworks
Work with R&D, CTO, and peer architects on system definition and trade-offs
Engage with customers and partners to shape system requirements and use cases