Machine Learning Researcher (1B2B94D)

Referment; New York (NY); On-site; USD 140,000 - 190,000; Full time

Job summary

Referment is seeking a machine learning researcher for complex quantitative research in systematic investing. You will own end-to-end experiments from hypothesis to scalable deployment, applying rigorous empirical methods to real-world data.

We value deep expertise in ML, strong mathematical foundations, and proficiency in Python with PyTorch or JAX. You will collaborate with researchers and engineers to translate research into production-ready solutions.

Qualifications

• Deep expertise in machine learning and solid mathematical foundations.
• Strong programming skills and ability to translate research into scalable code.
• Experience with PyTorch or JAX and performance optimization is valuable.

Responsibilities

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

Skills

Machine Learning

Python

Experimentation

Statistical thinking

Education

Bachelor's degree or higher

Tools

PyTorch

JAX

CUDA

Job description

Referment is working with a global investment and technology firm to hire a machine learning researcher for complex quantitative research in systematic investing. The role offers end-to-end ownership, from forming a hypothesis and designing experiments through to scalable implementation and real-world application.

You will combine deep machine learning knowledge with strong empirical judgment. The work includes exploring new architectures and training methods, testing whether improvements genuinely generalise, and solving computational bottlenecks when larger-scale research demands it.

The Role

• 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.

What We're Looking For

• A bachelor's degree or higher and 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.
• Practical 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.

This could suit an applied ML researcher or research engineer who combines publication-quality experimentation with production-minded technical execution.

#Referment