PhD Residency - Graph, Operations Research and Reinforcement Learning, Early Sta

Position: 2026 PhD Residency - Knowledge Graph, Operations Research and Reinforcement Learning, Early Sta[...]

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

Join to apply for the 2026 PhD Residency - Knowledge Graph, Operations Research and Reinforcement Learning, Early Stage Project role at X, The Moonshot Factory
.

General description:

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

Responsibilities

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

Location, Start Date, Duration

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

Expectations During the Residency

• Embedded into one of our confidential or public X projects.
• Compensated competitively and receive benefits.
• Part of a lively community of AI and ML Residents.
• Attend tech‑talks with AI leaders from across X.

Qualifications

• 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.

Preferred (It’d Be Great If You Also Had These)

• Open‑source projects 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.

Salary and Benefits

The US base salary range for this position is $109,000 - $150,000 + 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. The compensation details listed in US role postings reflect the base salary only, and do not include benefits.

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