Overview
On Site
Full Time
Skills
FOCUS
Scalability
Collaboration
Distributed Computing
Benchmarking
Extract
Transform
Load
Training
Workflow
Data Management
Performance Tuning
Software Engineering
Python
Evaluation
Trading
Innovation
Machine Learning (ML)
Artificial Intelligence
Research
Recruiting
Job Details
Overview
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.
What You'll Do
What we're looking for
Why Join Us?
Susquehanna is a global quantitative trading firm that combines deep research, cutting-edge technology, and a collaborative culture. We build most of our systems from the ground up, and innovation is at the core of everything we do. As a Machine Learning Systems Engineer, you'll play a critical role in shaping the future of AI at Susquehanna - enabling research at scale, accelerating experimentation, and helping unlock new opportunities across the firm.
If you're a recruiting agency and want to partner with us, please reach out to . Any resume or referral submitted in the absence of a signed agreement will not be eligible for an agency fee.
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.
What You'll Do
- 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
What we're looking for
- 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
Why Join Us?
Susquehanna is a global quantitative trading firm that combines deep research, cutting-edge technology, and a collaborative culture. We build most of our systems from the ground up, and innovation is at the core of everything we do. As a Machine Learning Systems Engineer, you'll play a critical role in shaping the future of AI at Susquehanna - enabling research at scale, accelerating experimentation, and helping unlock new opportunities across the firm.
If you're a recruiting agency and want to partner with us, please reach out to . Any resume or referral submitted in the absence of a signed agreement will not be eligible for an agency fee.
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