SOC Architect, XProf

Google•Sunnyvale, CA

About The Position

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward. With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions. Our team is focused around driving continuous improvements to the machine learning software/hardware stacks through providing insightful performance debugging for workloads and custom kernels. We provide insights by summarizing different views of captured profile data - such as trace timelines, memory usage, Compiler profiles, ML graph summaries. The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide. We're the driving team behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more. The US base salary range for this full-time position is $147,000-$211,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google [https://careers.google.com/benefits/].

Requirements

Bachelor's degree or equivalent practical experience.
2 years of experience with Single-Level Cell (SLC), SOC performance, SOC architecture.
2 years of coding experience in one or more of the following languages: C, C++, Java, or Python
2 years of experience in the machine learning field.
Experience in experimental design, analysis, and performance tools.
Experience in performance debugging of single-node systems.

Nice To Haves

Experience with ML frameworks such as TensorFlow, JAX, and PyTorch, or ML compilers such Accelerated Linear Algebra (XLA).
Experience in releasing and supporting open-source projects.
Proven track record in open-source software development.

Responsibilities

Learn and build an intuitive understanding of existing data collection, analysis, and visualization workflows.
Support new and exciting ML paradigms (such as horizontal scaling for upcoming TPU chips) by making contributions across the end-to-end stack and analysis tools.
Partner with Product Area leads to understand model optimization use cases, drive cross functional efforts to deliver on chip profiling requirements, and propose new hardware features.
Collaborate across Hardware, Driver, Runtime, and Performance Analysis teams and many other stakeholders.