Jane Street is hiring a Machine Learning Engineer in Machine Learning — New York, New York, United States. The overview below is synthesized from the employer posting on janestreet.com: factual requirements and scope are preserved, but prose is rewritten with editorial context. Verify details and apply via the employer link.
About the Position
Company context from the listing:
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.
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.
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.
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 programhere.
About You
Company context from the listing:
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.
- 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
Build a standout application
For the Machine Learning Engineer at Jane Street, reviewers look for concise evidence over buzzwords. Mirror the language of the posting sparingly, quantify support or delivery outcomes, and show how you handled ambiguity, time-zone collaboration and user empathy. Keep your resume to impact, keep your cover note to one page, and link to artifacts — tickets resolved, docs shipped, dashboards owned — that prove you can operate in a fast-moving Web3 team. Prepare to discuss a time you turned a confusing user report into a clear fix and how you measure quality in support and operations.
Web3 hiring values reliability: on-time follow-through, clear writing, and a track record of improving runbooks and tooling. Treat the application as a work sample. For interviews, be ready to walk through how you prioritize across time zones, handle a difficult user, and decide when to escalate versus resolve directly. Show how you document decisions so the next teammate benefits.
In a distributed Web3 org, trust builds through written clarity. Use the cover note to demonstrate it.