ML Engineer - Systematic Equity Desk

Paragon Alpha - Hedge Fund Talent Business; New York (NY); On-site; USD 120,000 - 180,000; Full time

Job summary

Paragon Alpha - Hedge Fund Talent Business is seeking an elite Machine Learning Engineer to join a leading multi-strategy hedge fund platform in New York. This position focuses on developing production-grade models and designing ML pipelines for intraday trading strategies.

The ideal candidate will have strong Python skills, experience with LightGBM, XGBoost, and CatBoost, and a solid background in deploying ML models in production. This is a unique opportunity to have a direct impact on investment decisions within a newly formed quantitative team.

Qualifications

• Strong Python skills required.
• Experience deploying ML models in production.
• Expertise in financial time-series validation and model optimization.

Responsibilities

• Develop production-grade ML models.
• Design end-to-end ML pipelines.
• Engineer features from market microstructure data.

Skills

Python

Deploying ML models in production

Financial time-series validation

Model optimization

Market microstructure

Tools

LightGBM

XGBoost

CatBoost

Job description

Paragon are working with a leading multi-strategy hedge fund platform, a senior portfolio manager is scaling their desk and will be hiring an elite Machine Learning Engineer.

This role will sit within a systematic equities team in New York that is building and deploying ML-driven trading signals for intraday strategies.

The role focuses on developing production-grade models using LightGBM, XGBoost, and CatBoost, designing end-to-end ML pipelines, engineering features from market microstructure data, and partnering closely with quantitative researchers and portfolio managers to bring models into live trading.

We're particularly interested in candidates with strong Python skills, experience deploying ML models in production, and expertise in financial time-series validation and model optimization. Exposure to market microstructure, alpha research, or systematic trading strategies would be highly valued.

The position offers direct impact on investment decisions within a newly formed quantitative pod and significant ownership across the entire ML lifecycle.