Machine Learning Systems Engineer: Scalable Data Pipelines

Susquehanna International; Bala Cynwyd (PA); On-site; USD 140,000 - 190,000; Full time

Job summary

Susquehanna International is seeking a Machine Learning Systems Engineer to help build the data infrastructure powering AI research. You will develop reliable, high‑performance systems for handling large and complex datasets, emphasizing scalability and reproducibility.

You’ll partner with researchers to support experimental workflows and translate evolving needs into production‑ready solutions. The role focuses on optimizing compute performance across distributed systems and building

Qualifications

• Experience building and maintaining data pipelines and ETL systems at scale.
• Experience with large‑scale ML infrastructure and familiarity with training and inference workflows.
• Strong understanding of best practices in data management and processing.
• Knowledge of systems level programming and performance optimization.
• Proficiency in software engineering in Python.
• Understanding of AI/ML workloads, including data preprocessing, feature engineering, and model 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 their requirements into scalable, production‑grade systems for AI experimentation.
• Optimize resource utilization across distributed computing infrastructure through profiling, benchmarking, and systems‑level improvements.
• Implement low‑latency high‑throughput sampling for models.

Skills

Data pipelines

ETL at scale

ML infrastructure

Performance optimization

Systems programming

Python software engineering

AI/ML workflows

Job description

Susquehanna International is seeking a Machine Learning Systems Engineer to help build the data infrastructure powering AI research. You will develop reliable, high‑performance systems for handling large and complex datasets, emphasizing scalability and reproducibility.

You’ll partner with researchers to support experimental workflows and translate evolving needs into production‑ready solutions. The role focuses on optimizing compute performance across distributed systems and building