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# Machine Learning Systems Engineer

- **Company:** Susquehanna International Group, LLP
- **Location:** Bala Cynwyd, United States
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Big Data, Computer Programming, Data as a Services, Data Cleansing, Data Infrastructure, Extract Transform Load (ETL), Data Systems, Distributed Systems, Python (Programming Language), Machine Learning, Performance Tuning, Software Engineering, Feature Engineering, Model Validation, Data Management, Machine Learning Operations, Data Pipelines
- **Published:** September 23, 2026
- **Apply:** https://www.dice.com/job-detail/8f932e19-6fcf-4459-a148-101053734d08

## About the Role

* 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

## Description

We're looking for a Machine Learning Systems Engineer to help build the data infrastructure that powers our AI research. In this role, you'll develop reliable, high-performance systems for handling large and complex datasets, with a focus on scalability and reproducibility. You'll partner with researchers to support experimental workflows and help translate evolving needs into efficient, production-ready solutions. The work involves optimizing compute performance across distributed systems and building low-latency, high-throughput data services. This role is ideal for someone with strong engineering instincts, a deep understanding of data systems, and an interest in supporting innovative machine learning efforts., * 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 our distributed computing infrastructure through profiling, benchmarking, and systems-level improvements
 * Implement low-latency high-throughput sampling for models