Anthelion Capital is an investment and data science platform. We augment our fundamental investment core with data science to make investments across the capital structure. We are building a proprietary platform that runs the full investment lifecycle, from underwriting to portfolio management.

What you'll do. Own the quant engineering platform, end to end. You'll build and own the infrastructure our researchers and PMs depend on — the shared data layer, the backtester, the deployment path, and the monitoring that keeps live models honest. Quant developers and researchers sit side by side and write against the same systems, so what you build gets used the day you ship it.

What you'll own:

· The shared data layer — market and reference data ingestion, the feature/signal store, and the Dagster asset graph that orchestrates them, all point-in-time correct. This is the main overlap with research — you'll build it as shared, self-service infrastructure that researchers extend too.

· The backtesting and simulation engine.

· The portfolio-construction and optimization libraries PMs allocate through.

· The model deployment pipeline: promoting a model from research to production by configuration, not by rewriting.

· Monitoring and observability for live models and pipelines — the first line of defense when something drifts or breaks.

You'll also get exposure to risk-factor modeling and exposure analytics, and direct portfolio-manager support — strategy diagnostics, scenario analysis, and allocation questions.

We're looking for:

· New grad through experienced hires.

· Strong software engineering: Python plus at least one systems language, good design instincts, and the ability to build tooling other people depend on.

· Solid grounding in quantitative finance — you understand what a Sharpe ratio, a risk factor, a backtest, or a portfolio optimizer actually means and why it's built the way it is, not just how to implement it. This is a quant + developer role.

· Data engineering chops — pipelines, correctness under time (as-of-date / point-in-time), reliability.

· A platform mindset: repeatable, guard-railed, self-service tooling over one-off scripts.

· Nice to have: Dagster/Prefect, Azure, model-registry or feature-store experience, prior work at a quant/trading firm or a serious data platform, hands-on risk-modeling or portfolio-construction experience.

Additional Details:

Compensation: Base salary of $120,000 to $240,000 depending on experience. Eligible for performance based discretionary bonus.

Location : Onsite in Midtown, New York City at least 3 days per week.

Other : Must be authorized to work in the United States without employer visa sponsorship.