PhD Residency - AI Science, Early Stage Project

Internship Mountain View, CA

THE FOUNDATION OF AN AMAZING JOURNEY

Our goal at X is to make the world a radically better place. In order to do that we seek fresh unexpected perspectives, from different fields, and that's why we're excited about you.

Life here isn't easy, but it's fun. We're trying to build things most people can't even imagine, and we're doing it with the hope of making a huge, positive impact on the world. You'll be embedded into a moonshot project, where you'll partner with team members to solve key challenges.

This isn't your ordinary internship. You'll be positively challenged and pushed professionally, in ways that you may have never experienced. If this excites you - keep reading.

During your internship, you can expect:

• To be placed on one of our confidential or public X projects
• To get paid competitively and with Google benefits
• To be part of a lively community of other Interns and Residents
• To attend colloquia and discussions with team leads from across Google, Deep Mind and external organizations

Requirements:

• Must be actively enrolled in an academic program and working towards completing a PhD

How you will make 10x impact:

• Architect multi-agent systems to automate complex research and accelerate discovery.
• Fine-tune LLMs to parse unstructured data and uncover latent technological trends.

Details:

• Location:
  
  X's headquarters in Mountain View, CA
• Start Date(s):
  Target start date of January 2, 2027 (open to rolling/flexible start dates based on project needs, university sign-off, and visa timelines).
• Duration: a flexible 4-months to 1 year program based on project team needs and your availability

What you should have:

• Experience building and deploying autonomous AI systems or agentic workflows.
• Hands-on expertise in LLM evaluation and adaptation, advanced prompting, and large-scale data processing.
• Strong Python skills and experience working with large, heterogeneous datasets, APIs, databases, and data pipelines.
• Familiarity with time-series, sequential, or panel data modelling, including some combination of state-space models, Markov-switching models, survival/hazard models, change-point detection, or event prediction.
• Understanding of statistical learning and rigorous out-of-sample evaluation, including test/train splits over time, data leakage, calibration, and back-testing.
• Experience with data integration, entity resolution, and data provenance across heterogeneous sources.

It’d be great if you also had these:

• Passion for deep-tech, R&D life cycles, and tech commercialization.
• Background in cross-disciplinary sciences, economics, or complex systems modelling.
• Familiarity with network science, graph analytics, or knowledge graphs: citation networks, researcher networks, community detection, centrality, link prediction, graph embeddings.
• Exposure to causal inference/econometrics, including Granger causality, event studies, treatment effect thinking, and the distinction between prediction and causation.
• Knowledge of Bayesian modelling and uncertainty quantification.
• Interest or experience in scientific-paper evaluation, reproducibility, benchmarking, or automated verification of mathematical, empirical, or computational claims.
• Work experience outside of academia is plus.
• Preferred Qualifications:
  
  Ph.D. in a quantitative or computational field (e.g., Machine Learning / Artificial Intelligence / Sequence Modeling, Econometrics, Economics / Finance, Statistics / Data Science, Applied Mathematics / Computational Science, or Network Science / Complex Systems / Information Science).
• We also strongly encourage applicants with relevant research experience in related or interdisciplinary fields to…