About the role

Aplyr's Quick Take

This role is for a Machine Learning Engineer focused on building and maintaining ML infrastructure and enhancing research workflows at Jane Street. You'll be working in a fast-paced trading environment, applying various ML techniques to real-world problems, and collaborating with a team that values software engineering and system design.

Good fit

Ideal candidates have a strong mathematical background and experience in machine learning, particularly in building production systems. A curious mindset and a passion for problem-solving are essential, along with familiarity with ML frameworks like PyTorch.

Worth noting

The company encourages applicants without a finance background, which is uncommon in this field. The role emphasizes a collaborative and innovative culture, with a focus on rapid experimentation and feedback.

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 program, 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 1-2 dedicated to assessing ML engineering skills.

Learn more about Jane Street’s internship program 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 don't expect you to have a background in finance—we're more interested in how you think and learn than what you currently know. You should be:

An undergraduate or PhD student with practical experience training an ML model, working on an ML library, or optimizing 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

Skills & Tags

go machine learning ai data design

Aplyr's read

Jane Street is a leading quantitative trading firm where tech and finance intersect, attracting analytical minds who thrive in a fast-paced, data-driven environment.
Synthesized from recent postings & public sources

What's promising

•Jane Street offers a highly collaborative environment with a strong focus on mentorship and learning.
•The firm is renowned for its innovative use of technology in trading strategies.
•Competitive compensation packages reflect the high value placed on talent and expertise.

What to watch

•The high-pressure environment may not suit everyone, especially those seeking work-life balance.
•Limited public information about career progression and long-term employee retention.
•The firm's focus on quantitative skills may limit opportunities for those from non-technical backgrounds.

Why Jane Street

•Jane Street's flat organizational structure encourages open communication and idea sharing.
•The firm is known for its rigorous and intellectually challenging interview process.
•Strong emphasis on using cutting-edge technology to drive trading decisions.

Aplyr’s read is generated by AI from public sources. Was it useful?