Senior ML Systems Engineer — Scalable AI Data Pipelines

SIG; Philadelphia (Philadelphia County); On-site; USD 120,000 - 170,000; Full time

Job summary

Susquehanna is seeking a Machine Learning Systems Engineer to build scalable data infrastructure powering AI research. You’ll develop high-performance pipelines, optimize compute across distributed systems, and deliver low-latency data services for experimentation.

You’ll collaborate with researchers to translate requirements into production-grade systems and improve reliability and throughput in a fast-paced environment.

Qualifications

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

Responsibilities

• Design and implement high-performance data pipelines for large-scale datasets with 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 improvements.
• Implement low-latency, high-throughput sampling for models.

Skills

Data pipelines

ETL at scale

ML infrastructure

Profiling & benchmarking

Systems-level programming

Python

Data management

Distributed computing

Experimentation support

Job description

Susquehanna is seeking a Machine Learning Systems Engineer to build scalable data infrastructure powering AI research. You’ll develop high-performance pipelines, optimize compute across distributed systems, and deliver low-latency data services for experimentation.

You’ll collaborate with researchers to translate requirements into production-grade systems and improve reliability and throughput in a fast-paced environment.