Senior Research Scientist

• Define and pursue a long-term applied research agenda to overcome fundamental limitations of LLMs in long-horizon reasoning, planning, and tool use for complex software engineering tasks.
• Design and implement novel agentic architectures, exploring frontiers such as automated workflow optimization, multi-agent decomposition, and self-improvement.
• Lead research into synthetic data generation, verification methods/environments and rewards for building data and capability flywheels for Gemini improvement.
• Develop high-fidelity evaluation frameworks that measure agent impact to software quality and developer productivity.
• Integrate principles from adjacent domains like machine learning, natural language processing, program analysis, program repair, formal methods for addressing ambiguous tasks (such as semantic bug fixing, intent inference, task decomposition).

Minimum qualifications:

• PhD degree in Computer Science, a related field, or equivalent practical experience.
• 2 years of experience leading a research agenda.
• Experience in one or more of the following areas: machine learning, large language models, agentic AI (planning, tool use, memory), reinforcement learning (RLHF, RLVF, RLGF, offline RL), or statistical learning.
• One of the more scientific publication submissions for conferences, journals, or public repositories (such as NeurIPS, ICML, ICLR, etc.).

Preferred qualifications:

• Experience original, long-term research agenda, evidenced by first-author publications that have introduced novel ideas.
• Experience building, training, and post-training large language models (LLMs).
• Experience with program analysis, program synthesis, automated program repair, formal methods, or designing developer tools.
• Experience designing and implementing novel evaluation benchmarks for LLMs, generative code models or agentic systems.
• Experience in Python software engineering and modern ML frameworks like JAX and TensorFlow.

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.

Labs is a group focused on incubating early-stage efforts in support of Google’s mission to organize the world’s information and make it universally accessible and useful. Our team exists to help discover and create new ways to advance our core products through exploration and the application of new technologies. We work to build new solutions that have the potential to transform how users interact with Google. Our goal is to drive innovation by developing new Google products and capabilities that deliver significant impact over longer timeframes.
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; Boulder, CO, USA; New York, NY, USA.

• PhD degree in Computer Science, a related field, or equivalent practical experience.
• 2 years of experience leading a research agenda.
• Experience in one or more of the following areas: machine learning, large language models, agentic AI (planning, tool use, memory), reinforcement learning (RLHF, RLVF, RLGF, offline RL), or statistical learning.
• One of the more scientific publication submissions for conferences, journals, or public repositories (such as NeurIPS, ICML, ICLR, etc.).