Senior Software Engineer - MLOps

ZipRecruiter; London; On-site; GBP 70,000 - 100,000; Full time

Job summary

Une entreprise de finance quantitative de premier plan recherche un ingénieur ML Ops pour rejoindre une nouvelle équipe responsable des opérations ML sur une plateforme de recherche de pointe. Le candidat idéal possédera des compétences en Python et une expérience en ML Ops, et sera chargé de construire des pipelines robustes et évolutifs tout en surmontant des défis techniques complexes. Avec un environnement de travail stimulant et des investissements technologiques massifs, c'est une opportunité unique de participer à l'avenir de l'infrastructure ML.

Benefits

25 jours de congés

Allocation mensuelle de télétravail

Budget de £20/jour pour le déjeuner

Bureaux flambant neufs au centre de Londres

Environnement collaboratif avec des experts

Qualifications

• Expérience significative en ML Ops.
• Compétences solides en codage Python.
• Compréhension approfondie des points de douleur du cycle de vie du ML.

Responsibilities

• Construire un pipeline de recherche et de déploiement ML évolutif.
• Gérer de nouveaux jeux de données et construire des outils pour l'entraînement distribué.
• Développer des systèmes de déploiement et de support en production robustes.

Skills

Python

ML Ops

Data Engineering

Orchestration frameworks

Job description

Job Description

We’re looking for an experienced ML Ops engineer to join a newly formed team in a leading quant firm, responsible for ML Operations across a next- research platform.

This is a high-impact, greenfield role where you’ll help design and build the future of ML infrastructure—from how data is shared, to how models are trained, deployed, and supported in production. ML is central to the firm’s trading strategies, and the platform you help shape will directly empower researchers and drive real business outcomes. Expect high autonomy and deep technical challenges—off-the-shelf tools won’t cut it, so you’ll often build bespoke solutions to handle complex interdependencies in ML workflows.

The Role

As part of the ML Workflows team, you’ll take ownership of building a mature, scalable ML research and deployment pipeline. You’ll work across the full ML lifecycle, including:

• Ingesting and managing new datasets
• Building tools for distributed training and inference
• Creating robust deployment and production support systems
• You’ll leverage your ML Ops experience to assess the current landscape, identify gaps, and lead the technical direction of the new platform

Projects you’ll work on include:

• Implementing best-practice feature and model stores
• Proper versioning of features, data, and models
• Improving inference compute utilisation through smarter serving
• Building CI/CD pipelines for ML workflows
• Solving complex job orchestration for model training
• Developing tooling for robust validation, monitoring, and recovery in production

We’re looking for engineers who thrive on complex, open-ended challenges and want to set new standards for ML infrastructure.

You should have:

• Significant experience in ML Ops
• Strong coding skills in Python
• A deep understanding of ML lifecycle pain points and practical solutions
• Experience building systems for scaling training, versioning, and deployment
• Bonus points for experience with distributed compute, data engineering, and orchestration frameworks (e.g. Airflow, Ray, KubeFlow).

Why join?

• Top-tier quant finance firm with huge tech investment
• Competitive base salary + 50–100%+ annual bonus
• 25 days holiday, monthly WFH allowance, and £20/day lunch budget
• Brand new, world-class offices in central London
• Surrounded by some of the sharpest minds in engineering and research

If you're ready to have real influence, work on greenfield infrastructure, and shape the ML future in a top business —we’d love to hear from you.