Machine Learning Engineer (MLOps)

Machine Learning Engineer (MLOps) at Moneybox. Join to apply for this role.

About Moneybox: Moneybox believes that building wealth throughout life should be possible for everyone and is guided by the idea that wealth is about more than money. We launched in 2016 with our round-ups feature and now help more than 1.5 million people build wealth. We provide a service that supports customers through saving, investing, buying a home, and planning for retirement.

Job Brief: We are building Moneybox Aurora, a comprehensive AI system to guide users toward more confidence and peace of mind. The machine learning team develops models that power Aurora and the core decisioning frameworks that guide users while ensuring safety, reliability, and alignment with user interests. Models are hosted internally; we develop using Databricks@Azure and deploy through Databricks or directly on AKS. This role collaborates with ML engineering teams and SREs to manage infrastructure, observability, deployment and in-production model management.

• Work with other ML engineers to refactor and productionize models and algorithms to meet standards for performance, reliability, and maintainability.
• Streamline and optimize deployment pipelines for fast and reliable delivery of ML models.
• Develop challenger / champion testing frameworks and automated tests.
• Optimise infrastructure to reduce cost and increase reliability.
• Monitor model and infrastructure performance, manage alerting, and integrate with business processes.
• Collaborate with AI and decisioning teams to inform objective functions, content strategy and data strategy for long-term ML outcomes.

• Experience deploying production-ready solutions serving millions of users.
• Optimization mindset with a penchant for scalable, robust solutions.
• Systems thinker capable of designing scalable architectures for emerging systems.
• Thrives in a fast-paced startup environment and eager to learn.
• Comfortable with ambiguity.

• 1 year of industry experience in an MLE, ML Ops or applicable Dev Ops / Data Ops role.
• 2+ years of Python programming in a day-to-day setting.
• Experience with Docker for deployment.
• Experience with Git or other version control systems, including automated testing and CI/CD patterns.
• Cloud platform experience (Azure, AWS, or GCP).
• End-to-end understanding of ML concepts; you don’t need to train models, but should understand key ML concepts.

• Azure-specific experience.
• Kubernetes (k8s) deployment experience.
• PySpark or Databricks for data processing.
• PyTorch, TensorFlow or other ML frameworks.
• Datadog or other monitoring frameworks (Prometheus, Grafana, New Relic).
• Infrastructure management with Terraform.

• Opportunity to join a fast-growing, award-winning and ambitious business.
• Friendly, highly motivated team.
• Environment where you are listened to and can have an impact.
• Collaborative and inclusive culture.
• Competitive remuneration and company shares.
• Company pension, hybrid working, home office furniture allowance, Personal Learning & Development budget.
• Private Medical Insurance, Health Cash Plan, Cycle to work, Gympass, enhanced parental pay & leave, and generous holiday entitlement.
• London-based office near the Oxo Tower.

Moneybox promotes inclusion, diversity and equity for all. We encourage applicants to request adjustments and are willing to go the extra mile to ensure all applicants can present their full potential.

Hybrid policy with 2 days in the London office and 3 days from home. Roles labeled as hybrid or remote require candidates to be based within the UK.

At this time we cannot offer visa sponsorship for this role and cannot consider overseas applications.

Please note if offered a position, the offer is conditional on satisfactory pre-employment checks (e.g., criminal record and adverse credit history checks). As a regulated financial business, adverse financial history could affect suitability. If you are aware of anything that could affect your suitability, please let us know in advance. We collect and may share applicants’ data to manage recruitment activities, including transfers outside the EEA or US where applicable. If you are unsuccessful, we may keep your details on file unless you opt out. For contact, email or .