Senior Engineer, Systematic Research Technology
Senior Research Engineer - Systematic Data & Research Platform
We're partnered with a leading investment firm seeking a Senior Research Engineer to join a highly technical team focused on building scalable research infrastructure for quantitative analytics.
This role centers on developing Python-based tooling and data platforms that enable researchers to efficiently work with large-scale structured and unstructured datasets.
The ideal candidate brings strong experience in data-intensive systems, research workflow enablement, and production-grade data pipelines, with a high bar for quality and reliability. Exposure to machine learning infrastructure and efficient use of compute resources is highly valued.
Key Responsibilities
- Design and build Python-based research tools and data infrastructure
- Develop scalable frameworks for data processing, feature generation, and research workflows
- Own data curation processes across large financial and alternative datasets, ensuring consistency and usability
- Enhance pipelines for data ingestion, validation, transformation, and access
- Collaborate closely with research teams to translate analytical needs into robust engineering solutions
- Support ML workflows including experiment tracking, automation, and reproducibility
- Improve performance and efficiency of compute workloads, including GPU utilization where applicable
- Partner with broader engineering teams to deliver scalable, reliable, and efficient systems
Core Requirements
- Strong software engineering experience with Python as a primary language
- Background in building data platforms, research tooling, or large-scale data systems
- Experience working with complex datasets in a high-performance environment
- Strong focus on data quality, validation, and governance
- Familiarity with workflow orchestration and distributed data processing
- Exposure to machine learning infrastructure / MLOps concepts
- Experience optimizing compute resources for data or ML workloads
- Ability to work closely with technical stakeholders in a fast-paced environment
Nice to Have
- Experience in quantitative, systematic, or data-driven environments
- Exposure to alternative data or large-scale analytical ecosystems
- Familiarity with distributed compute, containerization, or cloud environments
- Experience supporting reproducible research or experimentation frameworks
What Success Looks Like
- Delivering high-quality, scalable Python-based systems
- Enabling efficient research across large, complex datasets
- Maintaining a high bar for data integrity and reliability
- Building strong partnerships with technical stakeholders
- Driving improvements in research productivity and infrastructure performance
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