Machine Learning Engineer

McGregor Boyall
City of London
2 days ago
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Role: Machine Learning Engineer (Quantitative Development)

Location: London

Industry: Systematic / Quantitative Hedge Fund

Working model: Hybrid


Overview

A leading global quantitative fund is hiring a Machine Learning Engineer to scale how machine learning models are built, shipped, and supported across research and trading. This is a hands on role spanning solution architecture, software engineering, and infrastructure, with a strong focus on ML workflows, AI tooling, and pragmatic enablement across teams.


You will sit at the intersection of researchers, developers, and platform, driving adoption of robust development standards, reliable deployment patterns, and repeatable tooling that makes model delivery faster and safer.


Responsibilities

  • Roll out automation and CI/CD workflows tailored to machine learning delivery
  • Improve engineering standards through clean code guidance and architecture training
  • Build and promote consistent developer environments and AI assisted development tooling
  • Partner with internal teams to scale platform capabilities and reliability
  • Maintain internal training codebases and documentation; contribute to learning programs
  • Support campus style engagement (events, hackathons, coding challenges) where relevant


Experience

  • Strong object oriented engineering in Python and or C++, with SOLID design principles
  • Practical CI/CD experience, Git workflows, and infrastructure as code exposure
  • Familiarity with Docker, Terraform, Kubernetes, and modern cloud services
  • Working knowledge of MLOps concepts and tooling (for example MLflow, Airflow)
  • Comfortable enabling others: documentation, training, workshops, and hands on support


Desirable

  • LLMops exposure (plus)
  • Experience using AI agent tools in real engineering workflows (for example Cursor, Claude Code, Codex)


Why this role

  • High impact: your work directly accelerates model deployment and platform reliability for core investment teams
  • Varied technical scope: architecture, software engineering, platform infrastructure, and developer experience
  • Genuine enablement mandate: raise standards and adoption through training and great tooling


Next steps

If you are a pragmatic engineer who enjoys building platforms that researchers actually want to use (and you can translate best practice into day to day execution), please apply directly with a CV.

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