Quant jobs / D. E. Shaw

Machine Learning Researcher

A machine learning research role at D. E. Shaw focused on new modelling approaches for systematic investing. The researcher owns work from hypothesis to implementation.

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.

Develop and test ML approaches for hard quantitative prediction problems
Design experiments that separate real improvement from noise and overfitting
Prototype and benchmark new model architectures on large financial datasets
Optimise training, including on GPUs, to make larger experiments feasible

What they ask for

Required

Deep ML expertise with quantitative research orientation
Maths and statistics behind modern ML
Rigorous experiment design
Strong programming
PyTorch or JAX

Preferred

Large-scale training
GPU computing
CUDA, XLA or Triton

Education: Bachelor's degree or higher
Experience: Experience developing and empirically evaluating sophisticated ML approaches
Languages and tools: Python

How to prepare

Train a modern architecture on noisy time-series data and test for overfitting carefully
Learn GPU performance basics and profile a PyTorch or JAX training loop
Study experiment design: ablations, baselines, and statistical tests for improvements
Read recent papers on representation learning and reproduce one

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 D. E. Shaw’s site before applying: requirements, location and pay can change after we check them.

View the posting on D. E. Shaw’s site ↗

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