Machine Learning Engineer

Jane Street
London, United Kingdom
On-siteCareer-pivot friendly

Who this role is best for

Undergraduate or postgraduate students with ML experience and a collaborative mindset will find this mid-level role in London's financial domain engaging.

Best fit for

  • Students with ML model training experience and a collaborative spirit will thrive in this London-based mid-level role.
    — “undergraduate, postgraduate or PhD student with practical experience training an ML model”
  • Individuals with strong programming skills and a curiosity for applying ML to noisy data will align well with this opportunity.
    — “top-notch programmer with a love for technology”

Things to consider

  • The role involves working on real-world ML projects with limited time for theoretical exploration.
    — “collaborating with you on real-world ML projects we actually need done”

How to stand out

  • Highlight prior experience with ML workflows and optimization in your resume and interview responses.
    — “practical experience training an ML model, working on an ML library, or optimising an ML workflow”
  • Demonstrate your ability to work with noisy financial data and explain your approach to handling it.
    — “understanding of the differences between textbook machine learning and its application to noisy financial data”
  • Emphasize your collaborative and humble attitude, as these are explicitly valued in the hiring process.
    — “collaborative spirit, and a passion for solving interesting problems”
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · IndividualLevel · Junior

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • Worked on real-world ML projects
  • Gained experience with GPU clusters
Typical background
Undergraduate or postgraduate studentBackground in machine learning

Skills & requirements

Required

Machine LearningProgrammingProblem-solving

Preferred

FinanceGPU Usage

Stack & domain

Machine LearningFinance

About the role

Original posting from Jane Street

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 Engineer while also providing a truly unparalleled educational experience. You'll be paired with full-time employees who act as mentors, collaborating with you on real-world ML projects we actually need done. Many classes and activities are shared with our Software Engineering interns, while others focus specifically on machine learning applications and techniques.

Machine learning is a critical pillar of Jane Street's global business. Our ever-changing trading environment serves as a unique, rapid-feedback platform for ML experimentation, allowing us to incorporate new ideas with relatively little friction. If you’d like to learn more, you can have a look at our Machine Learning page.

During the programme, you'll work on projects mentored closely by the full-time employees who designed them. Some projects consider big-picture questions that we're still trying to figure out, while others involve building something new. You will get access to our growing GPU cluster containing thousands of H100/H200/B200s and gain an understanding of the differences between textbook machine learning and its application to noisy financial data.

The interview process follows the same structure as our Software Engineering Intern interviews, with one key addition: after your initial technical coding interview over Zoom, you'll have an on-site interview with 2-4 technical rounds, including one dedicated to assessing ML engineering skills.

Learn more about Jane Street's internship programme here.

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, a collaborative spirit, 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, postgraduate or PhD student with practical experience training an ML model, working on an ML library, or optimising an ML workflow

A top-notch programmer with a love for technology

Intellectually curious, collaborative, and eager to learn

Humble and unafraid to ask questions and admit mistakes

Fluent in English

Source: Jane Street careers

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