ML Systems Engineer: Scalable Data Pipelines for AI

Susquehanna International Group, LLP; Lower Merion Township (Montgomery County); On-site; USD 140,000 - 210,000; Full time

Job summary

Susquehanna International Group, LLP is seeking a Machine Learning Systems Engineer to design and build reliable, high-performance data infrastructure for AI research. You will craft scalable pipelines, collaborate with researchers, and optimize compute across distributed systems to support rapid experimentation and production workloads.

This role emphasizes low-latency, high-throughput data services, strong software engineering in Python, and a deep understanding of ML workflows from

Qualifications

• Experience building and maintaining data pipelines and ETL systems at scale.
• Experience with large-scale ML infrastructure and familiarity with training/inference workflows.
• Strong understanding of data management and processing best practices.
• Knowledge of systems-level programming and performance optimization.
• Proficiency in Python software engineering.
• Understanding of AI/ML workloads including preprocessing, feature engineering and evaluation.

Responsibilities

• Design and implement high-performance data pipelines for processing large-scale datasets with an emphasis on reliability and reproducibility.
• Collaborate with researchers to translate requirements into scalable, production-grade systems for AI experimentation.
• Optimize resource utilization across distributed computing infrastructure through profiling and systems-level improvements.
• Implement low-latency, high-throughput sampling for models.

Skills

Data pipelines

ETL systems

ML infrastructure

Training and inference workflows

Data management

Systems programming

Python

AI/ML workloads

Job description

Susquehanna International Group, LLP is seeking a Machine Learning Systems Engineer to design and build reliable, high-performance data infrastructure for AI research. You will craft scalable pipelines, collaborate with researchers, and optimize compute across distributed systems to support rapid experimentation and production workloads.

This role emphasizes low-latency, high-throughput data services, strong software engineering in Python, and a deep understanding of ML workflows from