Citadel Securities
Miami, New York, US

Machine Learning Researcher - PhD Intern (US)

$4,300 - $5,800/wkPosted Jul 11, 2025WebsiteLinkedIn

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About this role

Job Description

At Citadel Securities, a leading global market maker, our team of quantitative researchers models the markets and brings trading strategies to life every day. Specifically, the goal of this team is to leverage and tailor the state-of-the-art machine learning and AI algorithms to modernize the quantitative trading industry. We’re looking for extraordinary and highly motivated researchers who are excited about solving challenging problems and iterating in a fast-paced environment.

As an intern, you’ll get to challenge the impossible in research through an 11 week program that will allow you to collaborate and connect with senior team members. In addition, you’ll get the opportunity to network and socialize with peers throughout the internship.

Our signature internship program takes place June through August. Occasionally, we can be flexible to other times of the year. You will be able to indicate your timing preference in the application.

Your Objectives

  • Use statistics, machine learning or AI (e.g. deep learning, NLP) to extract patterns from various kinds of datasets through innovative and rigorous research
  • Implement algorithms in high-quality code
  • Work with large data sets, including unconventional and unstructured data sources
  • Back-test models and document research findings

Your Skills & Talents

  • PhD degree in mathematics, statistics, physics, computer science, or another highly quantitative field
  • Advanced training and strong research track record in statistics, machine learning, AI, or another highly quantitative field
  • Hands on programming experience in scripting (e.g. Python) and/or compiled languages (e.g. C++)
  • A background demonstrating strong problem-solving skills
  • An ability to communicate advanced concepts in a concise and logical way
  • Proficiency in creating and using algorithms to meticulously investigate and work through large data or error-checking problems