Job description

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. Google Research addresses challenges that define the technology of today and tomorrow. From conducting fundamental research to influencing product development, our research teams have the opportunity to impact technology used by billions of people every day. Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field -- we publish regularly in academic journals, release projects as open source, and apply research to Google products. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google . • Design, build, and evaluate agentic AI systems for scientific discovery. • Bring in domain-specific tools, simulators, datasets, and models (e.g., genomics pipelines, structure prediction, physics-based simulation) so agents can use them reliably at scale. • Work with scientists inside and outside Google to choose high-impact problems, check agent-generated hypotheses, and close the loop between prediction and discovery. • Set and lead a research agenda. Identify new directions at the intersection of agents and science, publish in top Machine Learning (ML) venues, and help shape our long-term strategy for AI-accelerated science. • Own the robust, scalable infrastructure for agent execution, orchestration, and evaluation, and help research ideas become reliable systems that others can build on. • PhD degree in Computational Biology, Bioinformatics, Biophysics, Physics, Computer Science, or a related quantitative field, or equivalent practical experience. • Experience developing one or more of the following: autonomous agents, multi-agent workflows, or tool-using LLM systems. • One or more first-author scientific publications in peer-reviewed journals or conferences. Minimum qualifications: • PhD degree in Computational Biology, Bioinformatics, Biophysics, Physics, Computer Science, or a related quantitative field, or equivalent practical experience. • Experience developing one or more of the following: autonomous agents, multi-agent workflows, or tool-using LLM systems. • One or more first-author scientific publications in peer-reviewed journals or conferences. Preferred qualifications: • 2 years of experience leading a research agenda in computational biology, computational physics, or machine learning for science. • Experience developing and deploying machine learning models or LLM-based systems with modern frameworks (e.g., JAX, PyTorch, TensorFlow). • Coding experience in Python, C++, or a similar language. • Experience designing benchmarks, evaluations, or verifiable reward signals for scientific reasoning, data analysis, or code generation.

Requirements

Must-haves

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
Learn more about benefits at Google .
Design, build, and evaluate agentic AI systems for scientific discovery.
Bring in domain-specific tools, simulators, datasets, and models (e.g., genomics pipelines, structure prediction, physics-based simulation) so agents can use them reliably at scale.
Work with scientists inside and outside Google to choose high-impact problems, check agent-generated hypotheses, and close the loop between prediction and discovery.
Set and lead a research agenda. Identify new directions at the intersection of agents and science, publish in top Machine Learning (ML) venues, and help shape our long-term strategy for AI-accelerated science.
Own the robust, scalable infrastructure for agent execution, orchestration, and evaluation, and help research ideas become reliable systems that others can build on.
PhD degree in Computational Biology, Bioinformatics, Biophysics, Physics, Computer Science, or a related quantitative field, or equivalent practical experience.
Experience developing one or more of the following: autonomous agents, multi-agent workflows, or tool-using LLM systems.
One or more first-author scientific publications in peer-reviewed journals or conferences.
PhD degree in Computational Biology, Bioinformatics, Biophysics, Physics, Computer Science, or a related quantitative field, or equivalent practical experience.
Experience developing one or more of the following: autonomous agents, multi-agent workflows, or tool-using LLM systems.
One or more first-author scientific publications in peer-reviewed journals or conferences.
2 years of experience leading a research agenda in computational biology, computational physics, or machine learning for science.

Nice-to-haves

US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits
Experience developing and deploying machine learning models or LLM-based systems with modern frameworks (e.g., JAX, PyTorch, TensorFlow).
Coding experience in Python, C++, or a similar language.
Experience designing benchmarks, evaluations, or verifiable reward signals for scientific reasoning, data analysis, or code generation.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlllmpython

Platform: google
Effort: medium