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. 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.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 . • 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. • Explore techniques for scalable memory and context management that enable reasoning over large codebases (dynamic, structurally-aware context and memory). • 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. • PhD in Computer Science, a related field, or equivalent practical experience. • 2 years of experience leading a research agenda. • Experience with modern ML frameworks such as JAX or PyTorch. • Experience in one or more of the following areas: agentic AI (planning, tool use, memory), reinforcement learning (RLHF, RLVF, RLGF, offline RL). • One or more scientific publication submissions for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.). Minimum qualifications: • PhD in Computer Science, a related field, or equivalent practical experience. • 2 years of experience leading a research agenda. • Experience with modern ML frameworks such as JAX or PyTorch. • Experience in one or more of the following areas: agentic AI (planning, tool use, memory), reinforcement learning (RLHF, RLVF, RLGF, offline RL). • One or more scientific publication submissions 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. • Experience designing and implementing novel evaluation benchmarks for LLMs, generative code models or agentic systems. • Experience with program analysis, program synthesis, automated program repair, formal methods, or designing developer tools. • Experience building, training, and fine-tuning large language models.

Requirements

Must-haves

Learn more about benefits at Google .
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.
Explore techniques for scalable memory and context management that enable reasoning over large codebases (dynamic, structurally-aware context and memory).
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.
PhD in Computer Science, a related field, or equivalent practical experience.
2 years of experience leading a research agenda.
Experience with modern ML frameworks such as JAX or PyTorch.
Experience in one or more of the following areas: agentic AI (planning, tool use, memory), reinforcement learning (RLHF, RLVF, RLGF, offline RL).
One or more scientific publication submissions for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).
PhD in Computer Science, a related field, or equivalent practical experience.
2 years of experience leading a research agenda.
Experience with modern ML frameworks such as JAX or PyTorch.
Experience in one or more of the following areas: agentic AI (planning, tool use, memory), reinforcement learning (RLHF, RLVF, RLGF, offline RL).
One or more scientific publication submissions for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).
2 years of coding experience.
1 year of experience leading research efforts and influencing other researchers.

Nice-to-haves

US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Experience designing and implementing novel evaluation benchmarks for LLMs, generative code models or agentic systems.
Experience with program analysis, program synthesis, automated program repair, formal methods, or designing developer tools.
Experience building, training, and fine-tuning large language models.

Application prep

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Keywords to mirror

mlai

Platform: google
Effort: medium