Research Scientist, Science AI & Autonomous Agent Systems

Google LLC; Cambridge (MA); On-site; USD 147,000 - 210,000; Full time

Job summary

Google Research in Cambridge, MA seeks a Research Scientist to advance agentic AI for scientific discovery. You will design and deploy large-scale experiments, integrate genomics pipelines, structure prediction tools, and physics simulations to enable reliable agent-based reasoning across disciplines.

You will publish results and collaborate with partner universities to push the boundaries of ML for science, contributing to Google’s broader research agenda and products.

Qualifications

• PhD in Computational Biology, Bioinformatics, Biophysics, Physics, Computer Science, or a related quantitative field, or equivalent practical experience.
• Experience developing autonomous agents, multi-agent workflows, or tool-using LLMs.
• 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 ML for science.

Responsibilities

• Design, build, and evaluate agentic AI systems for scientific discovery.
• Bring in domain-specific tools, simulators, datasets, and models for scalable agent use.
• Collaborate with scientists to select high-impact problems and validate hypotheses.
• Set and lead a research agenda, publish in top ML venues, shape long-term strategy.

Skills

Autonomous agents

Multi-agent workflows

Tool-using LLMs

Publications

ML model deployment

Python

C++

Education

PhD in Computational Biology/related field

Tools

JAX

PyTorch

TensorFlow

Job description

Google Research in Cambridge, MA seeks a Research Scientist to advance agentic AI for scientific discovery. You will design and deploy large-scale experiments, integrate genomics pipelines, structure prediction tools, and physics simulations to enable reliable agent-based reasoning across disciplines.

You will publish results and collaborate with partner universities to push the boundaries of ML for science, contributing to Google’s broader research agenda and products.