2026 PhD Residency - Knowledge Graph, Operations Research and Reinforcement Learning, Early Stage Project

How you will make 10x impact: 

General description: A machine learning/artificial intelligence specialist with experience in LLM and reinforcement learning post training.

1. Collaborating with the X Project team to design and develop machine learning approaches/algorithms to support the project's mission
1. Understanding the project’s goals and challenges and conduct relevant literature surveys 
2. Suggesting agentic RL solutions in which machines/approaches/algorithms/models can apply to those challenges
3. Prototype/train/evaluate RL algorithms, tools and components of large scale AI systems
2. Engaging with the resident community during the program, engaging with the X community, attending colloquia and tech talks.

This project aims to push the limits of science and modeling as we know them and to prove how ML can radically accelerate our understanding of the world

• Location: X's headquarters in Mountain View, CA
• Start Date(s): ~January 2026
• Duration: a flexible full-time 4 mo. to 1 year program based on project team needs and your availability

Throughout your AI Residency you can expect:

• To be embedded into one of our confidential or public X projects
• To get paid competitively and receive benefits
• To be a part of a lively community of AI and ML Residents
• To attend tech-talks with AI leaders from across X

What you should have: 

• Currently enrolled in a PhD program in a STEM field such as CS, Physics, Engineering or Mathematics/Statistics with a strong interest in machine learning.
• Strong experience with one or more general purpose programming languages such as Python, C/C++. 
• Experience with reinforcement learning (RL) and large language models (LLMs).
• Ability to set up RL infrastructure and synthetic data generation pipelines.
• Familiarity with operations research (OR) problems and their formalization.
• Knowledge of basic ontologies and programmatically constructing knowledge graphs (KG) from structured and unstructured data.

It’d be great if you also had these: 

• Open-source projects that demonstrate relevant skills and/or publications in relevant conferences and journals (e.g. NeurIPS, ICML, ICLR, CVPR, ICCV, COLM, ACL/EMNLP, ICASSP).
• Experience with GRPO/DAPO based online reinforcement learning with LLMs(7B+) on multi-GPU settings.
• Ability to produce code (e.g., in Google OR-Tools) and define rewards for formalized optimization problems.
• Experience translating natural language into formal graph query languages (Cypher, SPARQL).
• Experience using frontier LLMs (e.g., Gemini-Pro) to generate large-scale, high-quality synthetic datasets with automated verification steps.

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