Machine Learning Engineer | Experienced Hire

Susquehanna International Group, LLP

1d • Onsite

About The Position

We’re looking for a versatile Machine Learning Engineer to help build and optimize the core infrastructure that supports our AI research. This role involves working across the stack—from data processing and training efficiency to low-level GPU programming and performance tooling. You'll contribute to systems that are reliable, scalable, and tuned for high performance, while enabling researchers to better understand and improve their models. Ideal candidates are comfortable navigating complex systems, enjoy working across domains, and bring a strong engineering mindset paired with an interest in supporting innovative machine learning efforts.
Why Join Us?
Susquehanna is a global quantitative trading firm that combines deep research, cutting-edge technology, and a collaborative culture. We build most of our systems from the ground up, and innovation is at the core of everything we do. As a Machine Learning Engineer, you’ll play a critical role in shaping the future of AI at Susquehanna — enabling research at scale, accelerating experimentation, and helping unlock new opportunities across the firm.
If you're a recruiting agency and want to partner with us, please reach out to [e-mail]. Any resume or referral submitted in the absence of a signed agreement will not be eligible for an agency fee.
#LI-Onsite

Requirements

• Experience with data pipeline development and ETL processes
• Possess strong systems programming skills and understand performance optimization
• Strong software engineering skills in Python
• Experience writing and optimizing custom GPU kernels
• Have contributed to observability, benchmarking, or performance-focused infrastructure at scale
• Familiarity of AI/ML workloads
• Comfortable working in ambiguous, fast-evolving environments and collaborating across disciplines

Responsibilities

• Design and implement high-performance data pipelines for processing large-scale datasets with an emphasis on reliability and reproducibility
• Apply the latest techniques to our internal training runs to achieve impressive hardware efficiency for our training runs
• Implement custom GPU kernels
• Create observability and benchmarking tools to help researchers understand the performance of their models/training runs
• Build and maintain secure sandboxed execution environments

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed