Senior Research Scientist, AI's Impact on the Software Development Life Cycle

Google•San Jose, CA

19d•$174,000 - $252,000

About The Position

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 works across all of Google to architect the foundational intelligence layer for Google's agentic ecosystem, engineering the data, insights, and feedback loops that empower developers and autonomous agents. Our work powers product decisions within our organization and helps Google to understand the impact of AI on the socio-technical systems that drive our business. We value technical curiosity, collaborative visioning, and a bias toward action that celebrates fast failures that lead to clear learnings. The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

Requirements

PhD degree in Economics, Computer Science, Machine Learning, or a related field.
2 years of experience leading a research agenda.
One or more publications in an economics or AI journal or conference (such as the ICSE or FSE).

Nice To Haves

Applied experience with NLP/LLM frameworks (e.g., embeddings, text classification, prompt engineering, fine-tuning/distillation) applied to quantitative research or social science problems.
Demonstrated proficiency in Python and standard scientific computing/ML libraries (pandas, NumPy, PyTorch/JAX, scikit-learn, statsmodels).
Deep theoretical and practical expertise in causal inference, experimental and quasi-experimental design, and econometrics.
Track record of developing ML solutions for real world problems.

Responsibilities

Take nebulous socio-technical AI impact questions and turn them into concrete, validated, measurement strategies. Scale research strategies through Data Science and UXR partnerships.
Apply causal frameworks (e.g., difference-in-differences, instrumental variables, regression discontinuity, synthetic controls, quasi-experiments) to understand AI impact from observational and behavioral data.
Advise Product areas and Product teams on data driven system improvements e.g., how to better utilize AI in the software development lifecycle, how to improve product strategy to meet new development approaches, etc.
Mentor junior quantitative researchers and data scientists, establishing best practices for statistical inference, study design, and empirical reproducibility.