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Machine Learning Engineer Jobs

Engineers who build, train, and deploy machine learning models. A core role in the tech industry, driving innovation and solving complex problems.

Open roles
45
Salary range
£49k – £160k
Hiring companies
23
View 45 live roles → Get an alert

Machine Learning Engineers are at the heart of the tech revolution, combining software engineering with advanced data science to create intelligent systems. They work across a wide range of industries, from tech giants and scaleups to research-heavy startups and the larger consultancies. Their role involves designing, building, and deploying machine learning models that can process and learn from vast amounts of data, enabling applications from natural language processing to computer vision and beyond.

What the role does

Inside the role of a Machine Learning Engineer

A typical week for a Machine Learning Engineer is a mix of coding, model training, and collaboration with cross-functional teams.

  1. 01
    Design and implement machine learning models.
  2. 02
    Optimise algorithms for performance and scalability.
  3. 03
    Collaborate with data scientists and software engineers.
  4. 04
    Conduct experiments and validate results.
  5. 05
    Document and present findings to stakeholders.
  6. 06
    Stay updated with the latest research and tools.
Salary on the board

£49k – £160k

Based on advertised midpoints across the 91 UK listings priced in pounds in the last 12 months. Base salary only.

Salary visibility
5% of listings advertise a salary — up from 0% the year before.
By seniority
£k base
Mid
43
160
26 jobs
Senior
50
126
20 jobs
Lead
80
144
17 jobs
Skills & tools

What hiring managers ask for

% of 89 listings posted in the last 12 months that mention each skill, extracted from job descriptions.

Python
84%
Machine Learning
60%
PyTorch
44%
TensorFlow
37%
MLOps
35%
AWS
25%
Azure
25%
CI/CD
22%
SQL
20%
Kubernetes
19%
GCP
17%
Docker
15%
Career ladder

From Junior to Principal

A typical UK progression for machine learning engineers. Years are guidance — strong people move faster, and many senior folks sidestep into research, product or management.

  1. Level 1

    Junior Machine Learning Engineer

    0–2 yrs

    Assist in the development and testing of machine learning models, with a focus on learning and gaining hands-on experience.

  2. Level 2

    Machine Learning Engineer

    2–5 yrs

    Own the development and deployment of machine learning models, working closely with data scientists and software engineers.

  3. Level 3

    Senior Machine Learning Engineer

    5–8 yrs

    Lead the design and implementation of complex machine learning systems, guiding junior team members and driving innovation.

  4. Level 4

    Principal Machine Learning Engineer

    8+ yrs

    Strategise and oversee the machine learning initiatives of an organisation, influencing the direction of projects and mentoring the team.

Pathway

How to become a Machine Learning Engineer

There's no single route, but most people follow some version of these steps.

  1. 1

    Learn the Fundamentals

    Gain a strong foundation in programming, mathematics, and statistics. Familiarise yourself with key machine learning concepts and tools.

  2. 2

    Build Projects

    Apply your knowledge by working on real-world projects. This could be through internships, personal projects, or open-source contributions.

  3. 3

    Gain Industry Experience

    Start your career as a Junior Machine Learning Engineer, working on smaller projects and learning from more experienced colleagues.

  4. 4

    Specialise and Advance

    Develop expertise in specific areas of machine learning, such as deep learning or reinforcement learning. Take on more complex projects and leadership roles.

  5. 5

    Lead and Innovate

    As a Senior or Principal Machine Learning Engineer, lead major projects, mentor junior team members, and drive innovation within your organisation.

Live jobs

45 live roles

See all 45 roles →

Lead Machine Learning Engineer (Safety)

As a Lead Machine Learning Engineer, you will drive the technical strategy and deployment of secure AI systems across high-stakes domains. You will work closely with cross-functional teams, mentor engineering talent, and advise clients on AI safety and best practices.

Faculty London, United Kingdom
Hybrid Permanent

Senior Machine Learning Engineer (Safety)

As a Senior Machine Learning Engineer, you will lead the development and deployment of cutting-edge AI systems for high-impact missions in national security and AI safety. You will design and build scalable, production-grade ML software and infrastructure, mentor junior engineers, and collaborate with cross-functional teams to ensure AI systems are secure, trustworthy, and safe.

Faculty London, United Kingdom
Permanent

Principal Machine Learning Engineer

This role involves leading the technical direction of high-impact AI projects, particularly within financial services, by designing scalable machine learning systems and providing expert guidance across teams and clients. The engineer will architect complex data science frameworks, solve cross-cutting technical challenges, and influence company-wide strategy. Emphasis is placed on robust, production-grade AI solutions and advancing technical excellence in regulated environments.

Faculty AI London, United Kingdom
Hybrid Permanent

Senior Machine Learning Engineer

A Senior Machine Learning Engineer will lead the deployment of AI models into production environments for engineering and physics simulations, working closely with customers and cross-functional teams. The role involves building scalable ML pipelines, mentoring engineers, and translating R&D into practical tools using Python, PyTorch, and cloud/on-prem infrastructure. Frequent international travel and on-site collaboration are key aspects of the role.

PhysicsX London, United Kingdom

Senior Machine Learning Engineer

This role involves designing, building, and deploying machine learning models into production environments. You will work closely with data scientists and engineers to develop robust, scalable solutions that drive commercial outcomes, with a focus on MLOps, DevOps, and cloud platforms.

Harnham - Data and Analytics Recruitment London, United Kingdom £90,000 – £110,000 pa
Hybrid Permanent

Machine Learning Software Engineer (KTP Associate)

This role involves designing and implementing machine learning solutions to enhance decision-making and operational performance in a growing manufacturing supply business. The KTP Associate will work with complex datasets, lead data science initiatives, and introduce new ways of working to a traditionally operated company.

University of Essex Ipswich, South East England, United Kingdom £40 – £45 pa
Contract

Machine Learning Research Engineer

This role involves prototyping, building, and implementing new AI approaches, as well as evaluating their quality, efficiency, and feasibility. You'll work in a dynamic R&D team at a leading AI company, focusing on data-driven solutions and cutting-edge technologies.

Luminance Cambridge, United Kingdom
Hybrid Permanent

Senior Machine Learning Research Engineer

This role involves building and scaling generative and predictive machine learning models for cellular behaviour using multi-omic and interventional biological data. The engineer will implement large neural networks, optimise distributed training and inference pipelines, and collaborate closely with ML scientists and biologists to translate research into production-grade systems. Work focuses on robust infrastructure development, performance optimisation, and integrating cutting-edge ML tooling within an interdisciplinary team.

Relation Therapeutics London, United Kingdom
Permanent
See all 45 roles →
Hiring locations

Where this role is hiring

The locations with the most live listings for this role today.

London

Location
26 open

Cambridge

Location
3 open

Manchester

Location
1 open
FAQs

Common questions

  • Essential skills include programming (Python, R), mathematics (linear algebra, calculus), statistics, and knowledge of machine learning frameworks (TensorFlow, PyTorch).

  • Data Scientists focus on extracting insights from data, while Machine Learning Engineers build and deploy the models that power these insights. The roles often work closely together.

  • Responsibilities include designing and implementing machine learning models, optimising algorithms, collaborating with cross-functional teams, and staying updated with the latest research.

  • Salary ranges can vary widely based on experience, location, and industry. For more detailed information, refer to the salary section on this page.

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