ML Engineer | Alpha Research & Signal Generation | £900–£1,100/day | Contract | London

Systematic Hedge Fund. 6–9 months.

A systematic hedge fund is seeking a Machine Learning Engineer to work embedded within their quant research team — building, validating and productionising ML models for alpha signal generation, regime classification, and factor research across equities and futures.

This is not a generic ML engineering role. You will be working directly alongside quant researchers on models that feed live investment strategies. Candidates must have machine learning engineering experience gained within a systematic hedge fund, quant asset manager, or prop desk — specifically within a research function where ML models are used in the investment process. ML engineering experience from tech, retail, or enterprise environments is not sufficient. If you have not previously built or productionised ML models that feed into live trading strategies or systematic investment decisions, this role is not the right fit.

What they need:

→ 4+ years ML engineering within a systematic hedge fund, quant asset manager, or prop desk — building models used in live trading or investment research

→ Deep understanding of the challenges specific to financial ML: non-stationarity, lookahead bias, overfitting, regime changes, and transaction cost modelling

→ Strong Python — PyTorch or TensorFlow, scikit-learn, feature engineering pipelines

→ Experience with time-series ML methods applied to financial data — returns prediction, signal construction, regime detection

→ MLflow or equivalent for experiment tracking and model versioning in a research context

→ Familiarity with alternative data sources (NLP, pricing microstructure, cross-asset signals) beneficial

→ KDB+/q or Pandas/Polars for financial time-series data handling

Duration: 6–9 months.