Job Description

Systematic Hedge Fund. 6–9 months.

A systematic hedge fund expanding its machine learning research capabilities is seeking an MLOps Engineer to build foundational ML infrastructure — a feature store for systematic signals, a model training pipeline, and a model serving layer for live strategy deployment.

This role is for MLOps engineers who have built ML infrastructure specifically within a quantitative finance or systematic trading context — working directly alongside quant researchers to productionise signal models and research workflows. Generic enterprise ML infrastructure experience from retail, tech, or consulting is not sufficient. If you have not previously built ML pipelines that feed into live investment strategies or trading systems, this role is unlikely to be the right fit.

What they need:

→ 4+ years MLOps or ML infrastructure engineering within a systematic fund, quant asset manager, prop desk, or financial services firm where ML models are used in live trading or investment processes

→ Feature store design and implementation (Feast, Tecton, or equivalent) in a quantitative research context

→ Model training pipeline tooling: MLflow, Kubeflow, Vertex AI, or SageMaker

→ GPU cluster management and scheduling for ML training workloads

→ Python proficiency; familiarity with PyTorch or TensorFlow research workflows used by quant researchers

→ Kubernetes for ML workload orchestration

Duration: 6–9 months.