Machine Learning Engineer (Quant Finance)

Stabile Search•New York, NY

2d•Onsite

About The Position

My client, a core pillar of a leading quantitative trading firm, is scaling its research organization and is hiring Machine Learning Engineers to design, build, and scale the modeling systems that power research and production trading, working hand-in-hand with researchers rather than off to the side of them. As a Machine Learning Engineer, you'll work as a hybrid research-engineering partner embedded directly alongside researchers, developing model architecture, implementing and optimizing distributed training, building internal ML libraries and research tooling, and improving inference performance and scalability, all in service of deploying models into live trading across global markets. The division operates as one collaborative P&L rather than siloed books or pods: research is shared across teams, everyone pulls their own weight, and individual contribution is measured through year-end performance review rather than carved-out attribution. It's organized into several research teams of roughly 10-20 people each, evenly split between researchers and research engineers, with a majority of those teams focused on ML and deep learning.

Requirements

Bachelor's, Master's, or PhD in Computer Science, Engineering, Mathematics, Statistics, Machine Learning, or a related quantitative field
Strong Python skills, with experience in C++, CUDA, or other performance-oriented technologies
Proven experience designing, implementing, training, or optimizing machine learning models, particularly deep learning
Deep understanding of model architecture, training dynamics, and optimization techniques
Hands-on experience with PyTorch, TensorFlow, JAX, or similar ML frameworks
Experience building ML libraries, research tooling, or distributed training workflows
Comfort operating in Linux, high-performance computing environments
Strong collaboration and communication skills working alongside researchers and quantitative teams
Genuine interest in financial markets and quantitative investing, even without prior finance experience

Nice To Haves

Distributed training, model optimization, or ML infrastructure experience

Responsibilities

Design, build, and scale modeling systems that power research and production trading.
Develop model architecture.
Implement and optimize distributed training.
Build internal ML libraries and research tooling.
Improve inference performance and scalability.
Deploy models into live trading across global markets.