Machine Learning Engineer (Remote - UK)

Jobgether; United Kingdom; Remote; GBP 60,000 - 80,000; Full time

Job summary

A tech company in the UK seeks a Machine Learning Engineer to design and manage scalable ML systems for fraud detection. Your responsibilities include building pipelines, deploying models, and improving workflows in a collaborative environment. Experience in ML production environments and software engineering is crucial. The role offers flexible working hours, health insurance, and a wellness budget.

Benefits

Flexible working hours

Comprehensive health insurance

£1,000 annual wellness budget

25 days holiday plus bank holidays

Mental health support

Cycle-to-work scheme

Company-supported charitable initiatives

Qualifications

• Proven experience building and deploying machine learning models in production environments.
• Strong understanding of the ML lifecycle with design experience for large datasets.
• Solid grounding in data structures, design patterns, and testing.

Responsibilities

• Design, build, and manage scalable ML pipelines.
• Collaborate with data scientists to deploy ML models.
• Implement workflow orchestration for multi-stage ML jobs.

Skills

Building and deploying machine learning models

Workflow orchestration tools

Software engineering fundamentals

Analytical and problem-solving skills

Communication and teamwork skills

Tools

Prefect

Kubeflow

Argo

PyTorch

TensorFlow

Spark

Job description

This position is posted by Jobgether on behalf of Ravelin. We are currently looking for a Machine Learning Engineer in United Kingdom.

This role offers the chance to work at the intersection of data science and engineering, building and scaling high-performance machine learning systems that power fraud detection at a global level. As Machine Learning Engineer, you will be responsible for productionising cutting-edge models, designing efficient pipelines, and ensuring real-time predictions at scale. You’ll collaborate closely with data scientists and engineers to create robust ML infrastructure while contributing your expertise to improve processes, tools, and workflows. This is an opportunity to work with large-scale datasets, modern orchestration tools, and MLOps best practices, all while having ownership of impactful projects. You’ll thrive in an innovative, collaborative environment where your work directly protects businesses and customers worldwide.

Accountabilities:

• Design, build, and manage scalable end-to-end ML pipelines, from raw data ingestion to model inference, optimized for terabyte-scale datasets.
• Collaborate with data scientists to deploy performant, scalable, and maintainable machine learning models into production.
• Implement workflow orchestration for multi-stage ML jobs using tools such as Prefect or similar frameworks.
• Enhance and oversee MLOps infrastructure, including model versioning, automated deployments, monitoring, and observability.
• Troubleshoot and resolve performance bottlenecks in production systems while ensuring system availability.
• Continuously improve internal tools, engineering practices, and automation to streamline ML workflows.

• Proven experience building and deploying machine learning models in production environments.
• Strong understanding of the ML lifecycle, with hands-on experience designing training pipelines for large datasets.
• Familiarity with workflow orchestration tools such as Prefect, Kubeflow, or Argo.
• Solid grounding in software engineering fundamentals, including data structures, design patterns, Git, CI/CD, testing, and monitoring.
• Excellent analytical and problem-solving skills, with the ability to work in ambiguous situations.
• Strong communication and teamwork skills, with the ability to collaborate across technical and non-technical audiences.
• Nice to have: knowledge of a systems programming language (Go, C++, Java, Rust), deep learning frameworks (PyTorch, TensorFlow), distributed data processing (Spark, Dataproc), or data pipeline tools (dbt).

• Flexible working hours in a remote-first environment.
• Comprehensive BUPA health insurance.
• £1,000 annual wellness and learning budget.
• Monthly wellbeing and learning day (last Friday of each month).
• 25 days holiday plus bank holidays, plus 1 extra cultural day.
• Access to mental health support through Spill.
• Aviva pension scheme.
• Company-supported charitable initiatives and volunteering opportunities.
• Fortnightly randomised team lunches (in-person or remote).
• Cycle-to-work scheme.
• BorrowMyDoggy membership for dog lovers.
• Weekly board game nights and a dedicated social budget.

Jobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching.

When you apply, your profile goes through our AI-powered screening process designed to identify top talent efficiently and fairly.
Our AI evaluates your CV and LinkedIn profile thoroughly, analyzing your skills, experience, and achievements.
It compares your profile to the job’s core requirements and past success factors to determine your match score.
Based on this analysis, we automatically shortlist the 3 candidates with the highest match to the role.
When necessary, our human team may perform an additional manual review to ensure no strong profile is missed.

The process is transparent, skills-based, and free of bias — focusing solely on your fit for the role. Once the shortlist is completed, we share it directly with the company that owns the job opening. The final decision and next steps (such as interviews or additional assessments) are then made by their internal hiring team.

Thank you for your interest!

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