We are seeking a
SeniorResearch 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
