We are seeking a Senior Research Engineer to join our Systematic Technology team. This role will focus on building Python-based research tooling and infrastructure that enables quantitative researchers to work efficiently with large financial and alternative datasets.
The ideal candidate will have strong experience in data curation, research orchestration, and scalable data processing, along with a high level of attention to detail. Experience with MLOps and optimizing GPU usage for research workloads is also important.
Key Responsibilities
- Build and maintain research tooling and infrastructure in Python.
- Develop orchestration frameworks for large-scale data analysis, feature generation, and research workflows.
- Curate and manage broad financial and alternative datasets, with strong focus on quality, consistency, and usability.
- Improve data pipelines for ingestion, validation, transformation, and distribution.
- Partner with Quantitative Researchers to translate research needs into scalable engineering solutions.
- Support MLOps workflows, including automation, experiment management, and reproducibility.
- Optimize GPU integration and utilization across research workloads.
- Work with platform and infrastructure teams to ensure research systems are scalable, reliable, and efficient.
Required Qualifications
- Strong software engineering skills with Python as a primary language.
- Experience building research tooling, data infrastructure, or data-intensive platforms.
- Strong experience working with large financial and/or alternative datasets.
- Expertise in data curation and maintaining high standards of data quality.
- Experience with research orchestration and large-scale data processing workflows.
- Familiarity with MLOps practices and tooling.
- Experience supporting or optimizing GPU-based research or machine learning workloads.
- Strong attention to detail and ability to work closely with Quantitative Researchers.
Preferred Qualifications
- Experience in systematic investing or quantitative research environments.
- Familiarity with alternative data workflows and large-scale analytical platforms.
- Experience with distributed compute, workflow orchestration, and reproducible research environments.
- Exposure to cloud, containerization, or shared compute infrastructure.
Success in the Role
Success in this role will require:
- Strong Python engineering skills
- Excellent data quality and attention to detail
- The ability to support research at scale across large datasets
- Strong partnership with researchers
- Practical experience with MLOps and GPU optimization