Description

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
  
• 2 years of experience with software development in C .
• 2 years of experience with developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage or hardware architecture.
  
• 2 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.

Preferred qualifications:

• Experience with machine learning model training and serving.
• Experience with C development.
• Experience working across or understanding different parts of the software stack (e.g., ML frameworks, compilers, ML runtimes, or systems).
• Interest in compiler technology, ML runtime systems, or low-level software optimization.

About the jobJoin our team to improve the Accelerated Linear Algebra (XLA) compiler stack used for a wide range of machine learning models on TPU, GPU, and CPU hardware. You will work on projects to enhance compiler stability and usability across different frameworks and hardware, from research to production serving. Compiler expertise isn't required, making this a good project to onboard onto ML infrastructure for people with an interest in compilers and ML runtime systems.

Our team focuses on productionizing the integration of the XLA compiler and ML frameworks, critical for running machine learning models efficiently on Google's accelerator hardware (TPUs) as well as GPUs and CPUs. We work to standardize compiler interfaces and integration, improve stability and ensure model consistency between development and production. We collaborate with ML framework, compiler, runtime, and other infrastructure teams. Our efforts support most ML teams within Google and power Google Cloud's ML offerings.
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 force 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.

Responsibilities

• Write and test product or system development code.
• Understand how accelerator compilers and runtimes interact at a high level.
  
• Develop and apply metrics to understand the problem you are solving and gage status/success as needed.
• Close infrastructure (infra) gaps to help with ML stack maturation (e.g., reduce a number of ways something is done, improve reproducibility, improve tooling, improve usability).
• Participate in design reviews with peers and stakeholders to decide amongst available technologies.

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Requisition #: 108568947497804486

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