Staff Research Scientist, Machine Learning Efficiency
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Minimum qualifications:
- PhD in Computer Science, a related technical field, or equivalent practical experience.
- 4 years of experience with research agendas across multiple teams or projects in Machine Learning (ML) efficiency, optimization, or a related field.
- Experience with programming languages (e.g., Python, C or C++).
- Experience implementing, running, analyzing experimental models or transformer architecture.
- One or more scientific publication submissions for conferences, journals, or public repositories (e.g., ICML, ICLR, NeurIPS).
Preferred qualifications:
- Experience in innovative research.
- Experience as a leader within a research team.
- Ability to effectively navigate ambiguity.
- Knowledge of existing hardware capabilities and constraints.
- Understanding of chip design.
About the job
As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.
As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.
In this role, you will lead our Machine Learning (ML) efficiency effort. You will work on next-generation large language models architecture, in close partnership with hardware engineers and researchers. Recent breakthroughs in large language models performance led to many new research directions, and a lot of teams are focusing on maximizing performance when using the existing hardware. There are also teams designing the next generation of hardware based on an extrapolation of the current modeling efforts. You will co-design high-performance models along with the next generation of ML accelerators (TPUs) that can power them efficiently.
Google Research is building the next generation of intelligent systems for all Google products. To achieve this, we’re working on projects that utilize the latest computer science techniques developed by skilled software developers and research scientists. Google Research teams collaborate closely with other teams across Google, maintaining the flexibility and versatility required to adapt new projects and foci that meet the demands of the world's fast-paced business needs.
Responsibilities
- Generate, design ideas, leading a team for finding innovative solutions through theoretical or empirical insights.
- Design and implementing Machine Learning (ML) algorithms, running and analyzing experiments, incorporating components that could constitute part of a future Tensor Processing Unit (TPU).
- Collaborate with research teams located across the globe.
- Define long-term research agendas, intermediate milestones, and align with research and product partners.
- Impact through publication and scientific dissemination.
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