Quantitative ML Researcher - End-to-End Innovation

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 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.