Machine Learning Systems Engineer: Distributed Training

SIG; Philadelphia (Philadelphia County); On-site; USD 140,000 - 210,000; Full time

Job summary

Susquehanna is seeking a Machine Learning Systems Engineer to enhance distributed training infrastructure, enabling researchers to run large-scale experiments efficiently. You will work with researchers to optimize hardware utilization and build tooling for distributed workloads.

The role emphasizes Python software engineering, systems-level optimization, and profiling to improve performance across distributed training environments.

Qualifications

• Experience with large-scale ML training pipelines and distributed training frameworks.
• Strong software engineering skills in Python.
• Passion for diving deep into systems implementations and understanding fundamentals to improve their performance and maintainability.
• Experience improving resource efficiency across distributed computing environments by leveraging profiling, benchmarking, and implementing system-level optimizations.

Responsibilities

• Collaborate with researchers to enable them to develop systems-efficient models and architectures
• Apply the latest techniques to our internal training runs to achieve impressive hardware efficiency for our training runs
• Create tooling to help researchers distribute their training jobs more effectively
• Profile and optimize our training runs

Skills

ML pipelines

Python programming

Distributed systems

Performance optimization

Benchmarking

Resource profiling

Tools

Python

Distributed training

Profiling

Benchmarking

System optimization

Job description

Overview

We’re looking for a Machine Learning Systems Engineer to strengthen the performance and scalability of our distributed training infrastructure. In this role, you'll work closely with researchers to streamline the development and execution of large-scale training runs, helping them make the most of our compute resources. You’ll contribute to building tools that make distributed training more efficient and accessible, while continuously refining system performance through careful analysis and optimization. This position is a great fit for someone who enjoys working at the intersection of distributed systems and machine learning, values high-performance code, and has 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 Systems 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.

What You’ll Do

• Collaborate with researchers to enable them to develop systems-efficient models and architectures
• Apply the latest techniques to our internal training runs to achieve impressive hardware efficiency for our training runs
• Create tooling to help researchers distribute their training jobs more effectively
• Profile and optimize our training runs

What we're looking for

• Experience with large-scale ML training pipelines and distributed training frameworks
• Strong software engineering skills in Python
• Passion for diving deep into systems implementations and understanding fundamentals to improve their performance and maintainability
• Experience improving resource efficiency across distributed computing environments by leveraging profiling, benchmarking, and implementing system-level optimizations

About Susquehanna

Susquehanna is a global quantitative trading firm powered by scientific rigor, curiosity, and innovation. Our culture is intellectually driven and highly collaborative, bringing together researchers, engineers, and traders to design and deploy impactful strategies in our systematic trading environment. To meet the unique challenges of global markets, Susquehanna applies machine learning and advanced quantitative research to vast datasets in order to uncover actionable insights and build effective strategies. By uniting deep market expertise with cutting-edge technology, we excel in solving complex problems and pushing boundaries together.