Anthelion Capital
Quant Developer / Quant Research Intern - 2026/2027
About this role
Anthelion Capital seeks quantitative interns to develop core infrastructure for their systematic investment platform, working on data ingestion, factor modeling, backtesting engines, and research-to-production tooling. No finance background required—the role prioritizes strong programming and quantitative fundamentals matched to real platform components.
What you'll do
- Build market and reference data ingestion pipelines with point-in-time correctness and as-of-date validation
- Develop and validate factor or risk models on the emerging platform with emphasis on robustness and out-of-sample performance
- Create tooling to promote signals from research to production through configuration-driven workflows
- Contribute to backtesting/simulation engines, feature/signal stores, or portfolio optimization components
- Work directly with the quant team on production-grade infrastructure within the investment lifecycle platform
What they're looking for
- Python programming
- Statistics and machine learning fundamentals
- Data engineering and pipeline design
- Backtesting and quantitative validation
- C++, Rust, or systems-level programming (preferred)
- Cloud platforms such as Azure (preferred)
- Orchestration tools like Dagster or Prefect (preferred)
Benefits
- 12-week paid onsite internship in Midtown Manhattan
- Competitive weekly stipend of $1,800–$2,000 depending on coursework
- Direct mentorship from quant team on real production systems
- Strong interns considered as primary pipeline for full-time conversion
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Anthelion Capital
Anthelion Capital builds an AI-driven investment platform supported by cloud-based infrastructure systems. They are hiring Infrastructure Engineers to design and operate containerized deployments, manage CI/CD pipelines, and ensure platform reliability.
View all jobs at Anthelion CapitalLikely interview questions
- Walk us through a personal project or competitive programming experience where you went deep—what was challenging and how did you solve it?
- Describe your experience with Python and any other programming languages; have you worked on data-heavy or systems-level projects?