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Research Scientist, Google Research

Google
Posted 18 hours ago, valid for 14 days
Location

Mountain View, CA, US

Salary

$174,000 - $252,000 per year

Contract type

Full Time

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Sonic Summary

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  • The position is for a Research Scientist at Google, requiring a PhD in Computer Science or a related field along with 2 years of experience leading a research agenda.
  • Candidates should have experience in core ML model development and LLM training, as well as a record of scientific publication submissions.
  • The role involves managing large-scale tests, developing new products and technologies, and contributing to the research community through findings publication.
  • The salary range for this position is between $174,000 and $252,000, plus a 15% bonus target, equity, and benefits.
  • Preferred qualifications include 2 years of coding experience and 1 year of experience leading research efforts.

Minimum qualifications:

  • PhD in Computer Science, a related field, or equivalent practical experience.
  • 2 years of experience leading a research agenda.
  • Experience in core ML model development.
  • Experience with LLM training and generative models.
  • One of more scientific publication submission(s) for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).

Preferred qualifications:

  • 2 years of coding experience.
  • 1 year of experience leading research efforts and influencing other researchers.

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.

This team measures and improves key capabilities like multi-turn, factuality, and search for Gemini, working at the frontier of post-training for large foundational models. Modern LLMs are required to support long interactions with users, such as chat and informational dialogs, code development, image editing and video sessions. In this role, you will research new approaches for analyzing, evaluating and training LLMs to enhance their multi-turn capabilities.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.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

  • Scope and drive research efforts to improve complex frontier Gemini capabilities, such as multi-turn, factuality and tool-use.
  • Review the latest literature to guide research and experimental directions.
  • Curate and generate data to evaluate and improve Gemini capabilities.
  • Design and implement both human and automated evaluation strategies.
  • Design and conduct supervised fine-tuning and reinforcement learning experiments to improve the performance of Gemini capabilities such as multi-turn and factuality.



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