Machine Learning Engineer

Smartnumbers

Machine Learning Engineer developing models for cloud-based fraud systems. Collaborating on data science research and platform implementation with a focus on AWS services.

Posted 6/4/2026full-timeLondon • 🇬🇧 United KingdomJuniorMid-Level💰 £55,000 per yearWebsite

Tech Stack

Tools & technologies

AWSCloudPandasPythonScikit-LearnSQL

About the role

Key responsibilities & impact

You will be part of a cross-functional team, working across a variety of tasks from data science research and model development through to platform implementation and maintenance.
You will use your knowledge of machine learning algorithms, frameworks, and methodologies to research and develop models for our cloud-based authentication and fraud systems.
Explore and visualise data to discover innovative features and potential data sources.
Engineer datasets, develop data pipelines, perform feature engineering, and write code to train, deploy, monitor, and run real-time inferences.
Build and monitor ML models, addressing issues such as overfitting, underfitting, data leakage, and drift.
Use your expertise in engineering and DevOps/MLOps to manage our machine learning platforms using AWS SageMaker and other AWS services.
Design, build, and improve scalable public cloud-based machine learning platforms.
Develop robust data pipelines and workflows, contributing to platform reliability, scalability, and observability through effective monitoring, alerting, and performance tuning.

Requirements

What you’ll need

2 to 3 years’ commercial experience across a range of platform engineering and data science responsibilities.
Collaborative approach to working.
Able to own tasks end-to-end, take responsibility for the quality of deliverables.
Understanding of machine learning fundamentals: data analysis, feature engineering, algorithms, performance metrics etc.
Understanding of software engineering fundamentals: clean code, source control, SOLID principles, design patterns, refactoring etc.
Understanding of DevOps/ MLOps practices: Infrastructure as Code, data pipelines, CI/CD, containerisation, orchestration/pipelines, system & model monitoring.
Comfortable digging deep into either datasets or system logs to understand root causes or improve system performance.
Proficient in Python, SQL, and data/ML frameworks like Pandas, Scikit-Learn etc.
Experience with ML techniques and strategies, such as classical ML, deep learning, clustering, ensembling etc.
Experience with MLOps techniques and building and maintaining scalable data pipelines and ML platforms.
Experience with cloud services (preferably AWS) and infrastructure as Code (e.g. CDK, CloudFormation).

Benefits

Comp & perks

Hybrid working style, with the expectation of two days in the office (with a great City of London office base!)
Family friendly benefits including paid parental leave policies
An extensive health insurance policy for you, with an option to add your family members
A workplace pension with Hargreaves Lansdown
Life insurance of 4 x your salary
A discretionary annual bonus of up to 10% of your salary
Weekly self-development time to spend exploring your professional development interests
25 days of annual leave (plus bank holidays), your birthday off, and an opportunity to buy up to 5 days annual leave per year
A holistic wellbeing support plan encompassing a variety of offerings to assist you.
We provide you with a monthly £50 allowance to fund activities to best support your wellbeing as well as workshops and training to provide tools and guidance.
Additionally, there is a wide-ranging employee assistance programme available to advise on personal, family or financial matters and also fun social events during the year.

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Hard Skills & Tools

machine learning algorithmsdata analysisfeature engineeringPythonSQLPandasScikit-Learndeep learningclusteringMLOps

Soft Skills

collaborative approachownership of tasksresponsibility for qualityproblem-solvingattention to detail