Applied ML Researcher for Quantitative Investing

Referment; New York (NY); On-site; USD 150,000 - 210,000; Full time

Job summary

Referment is seeking a machine learning researcher for complex quantitative research in systematic investing. You will own projects end-to-end, from forming hypotheses and designing experiments to scalable implementation and real-world application.

You will combine deep ML knowledge with empirical judgment, explore new architectures and training methods, test generalisation, and tackle computational bottlenecks for large-scale research.

Qualifications

• Bachelor's degree or higher with deep expertise in machine learning.
• Strong mathematical and statistical foundations, with excellent experimental judgment.
• Excellent programming skills and the ability to take research from concept to scalable implementation.
• Experience with PyTorch or JAX; CUDA, XLA, Triton, large-scale training or performance optimisation would be valuable.
• Intellectual curiosity and an interest in applying ML advances to difficult quantitative problems.

Responsibilities

• Develop and evaluate machine learning approaches for challenging quantitative research problems.
• Form hypotheses and design rigorous experiments around predictive performance, robustness and generalisation.
• Explore new model architectures, representations, objectives and learning techniques.
• Prototype and benchmark ideas using large financial and other complex datasets.
• Improve training and implementation efficiency, including GPU optimisation where useful.
• Work with quantitative researchers and developers to move successful research towards application.

Skills

Machine learning

Experiment design

Research engineering

Python

Education

Bachelor's degree or higher in a quantitative field

Tools

PyTorch

JAX

CUDA

Job description

Referment is seeking a machine learning researcher for complex quantitative research in systematic investing. You will own projects end-to-end, from forming hypotheses and designing experiments to scalable implementation and real-world application.

You will combine deep ML knowledge with empirical judgment, explore new architectures and training methods, test generalisation, and tackle computational bottlenecks for large-scale research.