Job Description

As a backend ML engineer, you will sit on a product team developing AI, ML, and optimization products that are of strategic importance to the firm. You will partner with our product managers, researchers, and stakeholder teams to develop production-grade model implementations and infrastructure that facilitate the use of our models and analytics within a larger, integrated workflow. This includes implementing the methods being developed by the researchers in production-grade code and building surrounding infrastructure such that the analytics may be accessed via a variety of access patterns (API, files, etc.). You will also be responsible for creating automated tests of all software developed, including unit and integration tests.

Responsibilities:

Accurately implement methods described in technical documentation developed by our researchers.
Develop accompanying infrastructure to make the models and analytics developed accessible via a variety of access patterns.
Test all software being developed for accuracy and stability.

Qualifications

A bachelor's, master's, or Ph.D. in computer science or similar software engineering-focuseded major. A second major, minor, or degree in an adjacent quantitative discipline, such as math, engineering, or physics is a big plus.
2+ years of professional experience implementing machine learning models, optimizers, and other quantitative tooling in a professional setting.
The ability to read and understand mathematical documentation in the form of whitepapers containing the methods being developed.
Proficiency in Python in the contexts of automation, infrastructure development, and quantitative computing.
Strong understanding of Python scientific libraries such as Numpy, Scipy, Pandas, PyTorch, etc.
Understanding of testing in the context of software engineering, including unit testing, integration testing, performance testing, etc.
Background in finance will be looked upon favorably but is not a requirement.