Quant jobs / Susquehanna International Group (SIG)

Machine Learning Systems Engineer – Data

An engineering role building the data systems behind machine-learning research. It covers reliable pipelines, distributed compute and low-latency data services.

What you’d work on

Our reading of the posting: what a new hire in this role would most likely spend their first months on.

Build reproducible, high-throughput data pipelines for ML experiments
Turn researcher needs into production-grade systems
Profile and tune resource use on the distributed cluster
Build fast sampling services for models

What they ask for

Required

Data pipelines and ETL at scale
Large-scale ML infrastructure
Good data management practice
Systems-level performance work
Python
Feature engineering and model evaluation workflows

Education: No degree level stated
Experience: Experience building data pipelines and ML infrastructure at scale
Languages and tools: Python

How to prepare

Build a reproducible pipeline that versions datasets and feeds a training job
Profile data loading to find I/O and memory limits
Learn columnar formats and distributed processing
Study how training and inference use data differently

What to study

Apply

Read the full posting on Susquehanna International Group (SIG)’s site before applying: requirements, location and pay can change after we check them.

View the posting on Susquehanna International Group (SIG)’s site ↗

SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Susquehanna International Group (SIG) and doesn’t handle applications.