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, DeepMind 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 backtesting.
- 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 lifecycles, and tech commercialization.
- Background in cross-disciplinary sciences, economics, or complex systems modeling.
- 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 apply.
Additional public information:
- https://www.wired.com/video/watch/astro-teller-captain-of-moonshots-at-x-speaks-at-wired25
- https://www.bloomberg.com/news/videos/2019-10-10/alphabet-x-s-astro-teller-on-bloomberg-studio-1-0-video
- https://www.npr.org/2025/09/12/nx-s1-5493348/astro-teller-takes-us-inside-the-moonshot-factory-building-tech-ahead-of-its-time
- https://time.com/collections/best-inventions-2024/7094574/x-taara/
- https://youtu.be/_iLJU4HORAA?si=aK8cHf64NUNcsZXr
- https://www.fastcompany.com/best-workplaces-for-innovators/list/84
The US base salary range for this position is $109,000 - $157,000 + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include benefits.
Skills Required
- Actively enrolled in an academic program and working toward completing a PhD
- 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 with large, heterogeneous datasets, APIs, databases, and data pipelines
- Familiarity with time-series, sequential, or panel data modeling
- Understanding of statistical learning and rigorous out-of-sample evaluation, including temporal train-test splits, data leakage, calibration, and backtesting
- Experience with data integration, entity resolution, and data provenance across heterogeneous sources
- Passion for deep technology, R&D lifecycles, and technology commercialization
- Background in cross-disciplinary sciences, economics, or complex systems modeling
- Familiarity with network science, graph analytics, or knowledge graphs
- Exposure to causal inference or econometrics
- Knowledge of Bayesian modeling and uncertainty quantification
- Interest or experience in scientific-paper evaluation, reproducibility, benchmarking, or automated claim verification
- Work experience outside academia
- PhD in a quantitative or computational field
- Relevant research experience in related or interdisciplinary fields
X, The Moonshot Factory Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about X, The Moonshot Factory and has not been reviewed or approved by X, The Moonshot Factory.
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Fair & Transparent Compensation — Pay is considered competitive for core technical and senior roles, with employer-posted ranges and clear statements that total compensation includes base, bonus, equity, and benefits. Feedback suggests posted bands and explicit structure provide clarity on how pay is constructed.
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Parental & Family Support — Family support is described as generous, including paid parental leave, baby bonding, and transitional support for parents returning to work. Fertility treatments and maternity care are also covered, indicating depth in family-focused provisions.
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Retirement Support — Retirement programs include a 401(k) with a notable company match and immediate vesting of matched funds. Additional financial supports such as student loan reimbursement and coaching strengthen long-term financial security.
X, The Moonshot Factory Insights
What We Do
We create breakthrough technologies to help solve some of the world’s biggest problems. Born at Google, we got our start creating self-driving cars and smart glasses. Since then, we’ve continued to bring sci-fi ideas into reality.
