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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

Leads the technical direction of complex AI/ML projects, designing scalable production systems and guiding engineering teams in high-risk environments. Focuses on building robust ML infrastructure, CI/CD pipelines, and deploying AI platforms for government and public sector clients. Provides architectural leadership and mentors engineers while driving innovation in responsible AI.

Faculty London, United Kingdom
Hybrid Permanent Clearance Required

Principal Machine Learning Engineer

This role involves leading the design and implementation of scalable machine learning systems within high-stakes defence projects. You'll act as a technical authority, solving cross-cutting challenges, shaping architectural strategy, and mentoring engineering teams. The work combines advanced AI innovation with real-world deployment in secure, mission-critical environments.

Faculty London, United Kingdom
Hybrid Permanent

Senior Machine Learning Engineer

This role involves working on complex R&D projects in speech technology, collaborating with a diverse team of engineers, and staying current with the latest developments in machine learning. Responsibilities include training and deploying models, optimizing inference, and mentoring team members.

Speechmatics London, United Kingdom
Wayve logo

Principal Machine Learning Engineer GAIA

This role involves leading and improving the training and performance of Gaia, Wayve's world model for autonomous driving. Responsibilities include fine-tuning the model, enhancing its stability and coherence, and collaborating with research and engineering teams to drive measurable impact.

Wayve London, United Kingdom

Senior Machine Learning Engineer

Lead the design and deployment of production-grade machine learning systems for high-impact clients, particularly in the defence sector. Focus on scalable, ethical AI solutions using cloud platforms and modern MLOps practices. Mentor junior engineers and shape engineering standards while working across technical, product, and client teams.

Faculty AI London, United Kingdom
Hybrid Permanent Flexible Clearance Required

Lead Machine Learning Engineer

Lead Machine Learning Engineer to define technical direction for complex AI projects, design scalable ML systems, and guide delivery across high-risk, ill-defined environments. Will lead architectural decisions, mentor engineers, develop reusable tools, and act as a technical advisor to clients in regulated sectors. Focus on operationalising models using Python and major cloud platforms with containerised deployment via Docker and Kubernetes.

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 traveling globally to co-develop solutions on-site. The engineer will work with 3D data, CAE/CFD/FEA models, and time-series forecasting, translating R&D into robust, reusable tools.

PhysicsX England US$200,000 – US$250,000 pa
On-site Permanent Clearance Required

Senior Machine Learning Engineer

This role involves designing, building, and deploying high-performance machine learning solutions, working across the full ML lifecycle from experimentation to production. You will collaborate with engineering and AI teams to establish robust MLOps practices and optimize model performance in production environments.

Data Idols London, United Kingdom £80,000 – £90,000 pa
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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