Machine Learning Engineer (Quant Finance)

Are you a Machine Learning Engineer with distributed training, model optimization, or ML infrastructure experience looking to join one of the most sophisticated systematic trading businesses in the world, right here in New York City?

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.

If you're ready to bring your engineering skills into one of the most collaborative, model-driven trading environments in the industry and are available to move quickly, then this is the role for you.

What's the Job?

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. This is real modeling work, not infrastructure sitting apart from research.

My client's quantitative research division is fully systematic and fully automated, there are no portfolio managers and no fundamental or manual traders. Researchers and research engineers build the models, and the deployed models make the money, across all major asset classes and time horizons, from microseconds up to months.

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.

Compensation

Total compensation is calibrated to impact: offers up to roughly $2M are fair game for strong engineers.

Qualifications

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

Location

New York City.

Who are They?

My client is one of the most prestigious and sophisticated quantitative trading firms in the world, running a fully systematic, technology-driven investment business across every major asset class and time horizon. Their research culture is deliberately collaborative rather than siloed, built around shared research and a single P&L, and they compete aggressively for top-tier ML engineering talent alongside the world's leading AI labs.

To learn more, apply here today or email me at: Matt@Stabilesearch.com.