About this role

Google is hiring a mid-level Research Scientist in the machine learning function based in Toronto, Canada | Cambridge, MA | Mountain View, CA | New York City, NY. The posting calls out experience with LLMs, Machine Learning, NLP, Data Structures. Listed education preference: a Ph.D. or equivalent. Compensation is listed at $150,000–$154,000 per year.

Role: Research Scientist
Function: machine learning
Level: mid
Track: Individual contributor
Employment: Full-time
Location: Toronto, Canada | Cambridge, MA | Mountain View, CA | New York City, NY
Education: Ph.D. preferred
Posted: Jul 3, 2026

AI Summary

Research Scientist develops novel AI algorithms and LLM techniques through large-scale experiments and prototyping. Requires PhD in Computer Science or equivalent with published conference/journal submissions. Focus on algorithm discovery methods, LLM training, and program synthesis.

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Job description

from Google careers

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.

Our team conducts basic research into alternative AI paradigms beyond those currently trending. Our goal is to discover novel AI algorithms that can be efficient to run on typical or alternate computing substrates, using a mix of automated, hand-designed, and hybrid methods—specifically focusing on how advancing Large Language Model (LLM)-related techniques can accelerate this process.

In this role, you will research, develop, and publish breakthroughs in both algorithm discovery methods and the resulting algorithms themselves.

The Technology and Society organization connects research, people, and ideas across Google and Alphabet to help shape and advance our most ambitious technology innovations and initiatives and their impact on users and society for the better, and responsibly. In addition, we also aim to share perspectives, engage, and collaborate with others externally on technology related issues and opportunities for society.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

Canada: $150000 - $154000 (CAD) + 15% bonus target + equity + benefits
US: $147000 - $211000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

Carry out sustained exploratory research.
Review literature, identify key questions, design experiments, and interpret results.
Collaborate in person and remotely; maintain a respectful work environment.
Share ideas verbally and in writing; publish and present work at journals or scientific conferences.

Minimum qualifications:

PhD degree in Computer Science, a related field, or equivalent practical experience.
One or more scientific publication submission(s) for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).

Preferred qualifications:

Post-doctoral experience.
Experience in the field of machine learning.
Experience in the training and fine-tuning of LLMs.
Experience in the use of LLMs in fields of program synthesis or automated code discovery.
Excellent computer programming skills.
First-authored or last-authored publications in the field of machine learning at top venues (e.g., ICLR, ICML, NeurIPS).