Machine Learning Engineer

This is a rare opportunity to sit at the intersection of research, high-performance computing, and large-scale ML infrastructure, pushing the boundaries of ML applied to financial markets.

You wouldn’t be building models for one team, you’d be building the core deep learning library and infrastructure that powers the entire research engine of the firm.

What you’ll do

• Design, build, and evolve an internal deep learning library used firm-wide
• Partner directly with quantitative researchers to translate research needs into reusable ML tooling
• Work alongside HPC and systems experts to optimize large training workflows for speed and cost efficiency
• Integrate the best of open-source ML tooling with sophisticated in-house infrastructure
• Stay on top of cutting-edge advances in PyTorch, distributed training, CUDA optimization, and bring them into production internally

What makes this different

• Your work directly accelerates dozens of research teams rather than a single product
• Real scale: large models, serious compute, and complex distributed workloads
• A highly technical environment where ML engineering is treated as a first-class research enabler
• Direct exposure to some of the best quantitative researchers and systems engineers in the industry

Background they’re looking for

• 2\+ years of ML engineering / research engineering experience
• Strong Python plus CUDA and/or C\+\+
• Experience scaling distributed deep learning workflows (PyTorch or similar)
• Experience building ML tooling, libraries, or infrastructure rather than just training models
• Interest in high-performance systems and large-scale compute environments

Base: 250,000-350,000, total comp into the 7 figures