Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development, and with data structures/algorithms.
- 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
- 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 5 years of experience with one or more of the following: embedded system or lower level Software stacks (e.g., networking, storage, etc.), ML infrastructure, Compilers (e.g. XLA), or specialization in another ML field.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 3 years of experience in a technical leadership role leading project teams and setting technical direction.
- 3 years of experience working in a matrixed organization involving cross-functional, or cross-business projects.
About the job
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.
Google’s Cloud TPUs are designed to accelerate ML workloads, enabling the efficient training and deployment of advanced models. They support frameworks like TensorFlow, PyTorch, and JAX.
The team empowers businesses and researchers to take on real-world tests using ML techniques. As part of Google’s Core ML organization, Cloud TPUs play a key role in building a unified, cross-Google ML infrastructure to support both internal and external use cases. The team’s goal focuses on establishing this comprehensive ML ecosystem.
The team develops the infrastructure and tools required to bring Tensor Processing Unit (TPU) future New Product Introduction (NPIs) to Google Cloud Platform (GCP) customers from design, implementations and deployments. Their work spans hardware qualification, security verification, integration testing, profiling, tracing, and resiliency, ensuring seamless, high-quality TPU deployment for users.
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
The US base salary range for this full-time position is $197,000-$291,000 + 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
- Design, develop, test, deploy, maintain, and enhance large-scale Machine Learning (ML) infrastructure software solutions.
- Lead the design and implementation of solutions in specialized ML areas, optimize ML infrastructure, and guide the development of model optimization and data processing strategies.
- Architect, design, implement features, and scale the cloud TPU infrastructure to separate multiple generations of TPU hardware and products.
- Contribute to the key component of cloud TPU technical roadmap and architecture improvement.
- Develop the tools and automation to Qualify the Cloud TPU solution (security, performance, functions, etc.) faster for Cloud users to get the best experience of the accelerators for their workloads.
