Quant jobs / Jane Street

Machine Learning Research Engineer

A research engineering role bridging ML research and platform engineering on Jane Street's ML team. The hire helps choose modelling approaches and builds the workflows researchers use.

What you’d work on

Our reading of the posting: what a new hire in this role would most likely spend their first months on.

Build and maintain training and inference infrastructure
Prototype new model ideas alongside researchers
Build workflow tools that shorten research feedback loops
Advise on which model family fits a problem

What they ask for

Required

Experience building training and inference infrastructure
Strong maths: optimisation, regularisation, linear algebra
Fluency with an ML framework, ideally PyTorch
Ability to keep a reproducible research codebase
Knowledge of neural networks and tree-based models
Following current ML research

Preferred

Jax or TensorFlow

Education: No degree level stated.
Experience: Substantial machine learning and infrastructure experience
Languages and tools: Python
PyTorch
JAX
TensorFlow

How to prepare

Implement gradient-boosted trees and a small neural net from scratch to understand the maths
Build a reproducible training pipeline with config and experiment tracking
Review optimisation and regularisation theory
Read recent papers and reproduce one result

What to study

The NLP and ML Quant Researcher roadmap sets out the stages for this kind of role, with free resources for each.

Open the roadmap

Apply

Read the full posting on Jane Street’s site before applying: requirements, location and pay can change after we check them.

View the posting on Jane Street’s site ↗

SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Jane Street and doesn’t handle applications.