Research Intern

Quadrillion•New York City, NY

11h•Onsite

About The Position

We're hiring a Research Intern to help us answer a question no one has answered yet: how do you train a model that is genuinely good at research? This is hard, and largely uncharted. Very few groups have managed to teach LLMs to perform the open-ended, long-horizon reasoning that real research demands: forming a hypothesis, designing an experiment, reading the result, and deciding what to try next. We're doing it with a small team and a strong prior that research data can be used in a highly efficient and ingenious way. As a Research Intern, you'll own a real research question rather than a ticket. Depending on your interests, that might mean turning a corpus of arXiv papers into reinforcement learning environments, designing reward signals for ambiguous, long-horizon research tasks, building tight RL loops between user data and model training, or constructing research-focused benchmarks.

Requirements

Currently enrolled in a PhD program in Computer Science, Statistics, or Linguistics
Research experience in at least one area of machine learning or its neighbors (natural language processing, deep learning, reinforcement learning, optimization, statistics, etc.)
Ability to move quickly in a zero-to-one environment

Nice To Haves

A track record of research contributions, such as first-author publications at major ML conferences (NeurIPS, ICML, ICLR) or strong journals.
Hands-on experience training, fine-tuning, or benchmarking large modern models.
Enrolled at a degree program in the US or Canada, returning to the program after the internship.

Responsibilities

Post-train models for scientific and computational research tasks
Build evaluations and benchmarks for such tasks, and use them to measure frontier models and our own