Team Lead, Machine Learning Frameworks, RDNA
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Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
- 5 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), or specialization in another ML field.
- 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures/algorithms.
- 3 years of experience in a technical leadership role leading project teams and setting technical direction.
- 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
- Experience with TPUs, TPU system design, GPUs.
- Expertise in ML compilers and runtimes.
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.
With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.
The Runtime, Devices, and Numerical Acceleration (RDNA) team mission is end-to-end training and inference success for Google and the world, with cost-effective and performant software stacks across hardware, frameworks, and model types. The RDNA team seeks to enable users to develop and execute ML programs seamlessly across frameworks and heterogeneous hardware with performance, usability, and extensibility. We are building the ML infrastructure between the frameworks and hardware as a platform.
In this role, you will be responsible for designing the new generation of ML accelerator runtimes, inference and training infra at scale, and full-stack performance optimizations.
The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google 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 Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.
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
- Lead, develop and support training, and serve ML development programs and strategy.
- Develop stable, standardized APIs to generically represent and execute ML programs with an explicit goal to de-fragment the ML infrastructure, insulating users from low-level implementation details if they desire.
- Develop end-to-end solutions for Google’s ML ecosystem needs (across frameworks, hardware, and tooling).
- Strategize, support, and develop large-scale model training infrastructure.
- Migrate existing models across frameworks, runtimes, and product area’s custom workflows onto standardized ML infrastructure with minimal disruption.
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Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.
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