This is a high?impact, hands-on role within a small, elite team responsible for building and deploying advanced ML-driven trading signals and data?powered insights used across the companys core products.
You will work closely with a global manager and collaborate with two additional ML engineers, contributing directly to the next generation of data, modeling, and signal?generation infrastructure.
This position is ideal for someone who combines strong ML engineering fundamentals with curiosity for market data, experimentation, and production?grade deployment.
You will work closely with a global manager and collaborate with two additional ML engineers, contributing directly to the next generation of data, modeling, and signal?generation infrastructure.
This position is ideal for someone who combines strong ML engineering fundamentals with curiosity for market data, experimentation, and production?grade deployment.
Build and maintain complex SQL-based pipelines for modeling datasets
Design and execute large?scale experiments with proper cross?validation, leakage control, calibration, and reproducible backtesting
Develop ML models using state?of?the?art techniques and deploy them into production environments
Requirements:
Python + production experience:
Pandas/Polars, SQL/BigQuery, FastAPI, Docker, CI/CD
Modeling expertise:
XGBoost / PyTorch, calibration, class imbalance handling, walk?forward CV, leakage control
Experimentation at scale:
MLflow / DVC, reproducible backtests, GCP/Vertex jobs, orchestration frameworks
Capital markets:
Strong interest is required; understanding of equities/portfolio basics is important
(Algo?trading experience is an advantage but not mandatory)
Familiarity with quirks of financial data and portfolio construction (typically the longest to master)
Senior individual contributor
No people management
Full ownership across the ML lifecycle
Works closely with the ML/Quant team and global leadership
Python + production experience:
Pandas/Polars, SQL/BigQuery, FastAPI, Docker, CI/CD
Modeling expertise:
XGBoost / PyTorch, calibration, class imbalance handling, walk?forward CV, leakage control
Experimentation at scale:
MLflow / DVC, reproducible backtests, GCP/Vertex jobs, orchestration frameworks
Capital markets:
Strong interest is required; understanding of equities/portfolio basics is important
(Algo?trading experience is an advantage but not mandatory)
Familiarity with quirks of financial data and portfolio construction (typically the longest to master)
Senior individual contributor
No people management
Full ownership across the ML lifecycle
Works closely with the ML/Quant team and global leadership
This position is open to all candidates.
