Machine Learning Researcher Intern

Machine Learning Researcher, New York:: Jane Street Jane Street Group, LLC uses cookies and similar technologies, including third-party cookies, on this Site to provide basic functionalities and perform analytics. You may accept or decline cookies by selecting “Accept” or “Reject”. For information regarding our cookie practices, see Jane Street’s Ad and Cookie Policy. Accept All Reject All WHO WE ARE WHAT WE DO THE LATEST CULTURE JOIN JANE STREET mobile navigation menu toggle CLIENT OFFERING GLOBAL CAPITAL MARKETS

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OUR OFFICES STREET VIEW PUZZLES Join Jane Street OVERVIEW DEPARTMENTS OPEN ROLES PROGRAMS AND EVENTS INTERNSHIPS INTERVIEWING CONTACT US Disclosures & Policies Privacy Fraud and Impersonation Warnings Join Jane Street Open roles Job description Machine Learning Researcher Internship, May-August LOCATION NYC DEPARTMENT Trading, Research, and Machine Learning TEAM Machine Learning Apply Share this job About the Position Our goals are to give you a real sense of what it’s like to work at Jane Street as a Machine Learning Researcher while also providing a truly unparalleled educational experience.

You’ll work side by side with experienced ML Researchers on projects that we’ve selected for their combination of novel ML ideas and relevance to real-world systematic trading strategies. You'll learn how we think about markets through challenging classes and activities, and practice using established methods alongside our own unique twists to train practical models.

At Jane Street, the lines between research, technology, and trading are intentionally blurry, and you'll have access to petabytes of data, a computing cluster with hundreds of thousands of cores, and a growing GPU cluster containing thousands of high-end GPUs. Trading poses unusual challenges—large models and nonstationary datasets in a competitive multi-agent environment—that force us to search for novel techniques. You’ll spend the bulk of your internship working closely with full-time machine learning researchers on projects drawn from their own work.

You might conduct an end-to-end study of an unexplored dataset, try a new modeling paradigm for a thorny problem, or consider blue-sky approaches that we’re still trying to figure out. The problems we work on rarely have clean, definitive answers, and they often require insights from colleagues across the firm with different areas of expertise. Depending on the day, you might be diving deep into market data, tuning hyperparameters, debugging training issues, or analyzing the predictions your model makes.

Note that given the IP-sensitive nature of machine learning research at Jane Street, it is unlikely that any research findings associated with the internship will be suitable for outside academic publication. About You If you’ve never thought about a career in finance, you’re in good company. Many of us were in the same position before working here. If you have a curious mind and a passion for solving interesting problems, we have a feeling you’ll fit right in. We're more interested in how you think and learn than what you currently know.

You should be: An undergraduate, PhD student, or postdoc with practical experience working on ML problems Interested in applying logical and mathematical thinking to all kinds of problems Curious about the machine learning landscape and excited to apply state-of-the-art techniques drawn from many problem domains Fluent with a versatile set of models and tricks Able to rapidly implement and iterate on your ideas in Python and your favorite ML framework Eager to ask questions, admit mistakes, and learn new things If you’d like to learn more, you can read about our interview process and meet some of the team.

Learn more about Jane Street’s internship program here. Base salary is $300,000.

Jane Street is an Equal Opportunity Employer Apply Share this job Helpful Links web page Machine Learning at Jane Street Learn how Machine Learning is a part of our work and explore open roles, programs, and fellowships. podcast episode Finding Signal in the Noise: Machine Learning and the Markets podcast episode The Uncertain Art of Accelerating ML Models web page Internships at Jane Street GTKU Get to Know Us: JP talks Machine Learning GTKU Get to Know Us:

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