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 youre 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.

Whats the Job

As a Machine Learning Engineer youll 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 clients 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. Its 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

• Bachelors Masters 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 worlds leading AI labs.

To learn more apply here today or email me at: .