Machine Learning Engineer Jobs

Engineers who build and deploy machine learning models. A core role in the AI ecosystem, combining software engineering with data science.

Open roles
45
Salary range
£35k – £160k
Hiring companies
17

Machine Learning Engineers are at the heart of the AI revolution. They design, build, and deploy machine learning models that power everything from recommendation systems to autonomous vehicles. These roles are found in a wide range of organisations, from tech giants and research-heavy startups to scaleups and the larger consultancies. The work is highly technical, requiring a deep understanding of both software engineering and data science principles.

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 data scientists and other engineers.

  1. 01
    Develop and optimise machine learning models.
  2. 02
    Collaborate with data scientists to refine datasets.
  3. 03
    Integrate models into production systems.
  4. 04
    Monitor and maintain model performance.
  5. 05
    Document and present findings to the team.
  6. 06
    Stay updated with the latest research and tools.
Salary on the board

£35k – £160k

Based on advertised midpoints across the 22 priced listings posted in the last 12 months. Base salary only.

By seniority
£k base
Entry
39
40
1 job
Mid
35
160
13 jobs
Senior
50
120
5 jobs
Lead
90
120
2 jobs
Skills & tools

What hiring managers ask for

% of 51 listings posted in the last 12 months that mention each skill, extracted from job descriptions.

Python
82%
Machine Learning
65%
PyTorch
63%
TensorFlow
53%
AWS
35%
Kubernetes
31%
GCP
29%
MLOps
29%
Docker
27%
Azure
24%
Pandas
16%
Scikit-learn
16%
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 other engineers.

  3. Level 3

    Senior Machine Learning Engineer

    5–8 yrs

    Lead the design and implementation of complex machine learning systems, mentoring junior engineers and driving innovation.

  4. Level 4

    Principal Machine Learning Engineer

    8+ yrs

    Strategise and oversee the AI roadmap, influencing the direction of the organisation's machine learning efforts and leading large teams.

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 building machine learning projects. This could be through personal projects, internships, or university coursework.

  3. 3

    Gain Industry Experience

    Start your career as a Junior Machine Learning Engineer, working on real-world problems and learning from experienced colleagues.

  4. 4

    Specialise and Advance

    Develop expertise in specific areas of machine learning, such as natural language processing or computer vision. Progress to more senior roles.

  5. 5

    Lead and Innovate

    Take on leadership roles, driving the development of cutting-edge AI solutions and mentoring the next generation of machine learning engineers.

  6. 6

    Influence Strategy

    Shape the AI strategy of your organisation, influencing key decisions and leading large-scale machine learning initiatives.

Live jobs

45 live roles

See all 45 roles
Faculty AI logo

Machine Learning Engineer

As a Machine Learning Engineer, you will work on delivering bespoke AI solutions for diverse clients, focusing on scalable software architecture and best practices. You will collaborate with cross-functional teams to ensure the technical feasibility and timely delivery of high-quality ML systems, and act as a technical advisor to clients and partners.

Faculty AI London, United Kingdom
Hybrid Permanent Flexible Clearance Required
Faculty AI logo

Machine Learning Engineer

This role involves building and deploying production-grade machine learning systems for high-impact clients, particularly in the defence sector. You'll work across the full ML lifecycle, from scoping and design to implementation, ensuring scalable, ethical AI solutions are delivered with technical excellence. Collaboration with cross-functional teams and direct client engagement are key aspects of the position.

Faculty AI London, United Kingdom
Remote Permanent Flexible Clearance Required
PhysicsX logo

Machine Learning Engineer

As a Machine Learning Engineer, you will work closely with Data Scientists, Simulation Engineers, and customers to understand and solve complex engineering and physics challenges. You will design, build, and test reliable and scalable ML data pipelines, manipulate 3D point cloud and mesh data, and create reusable libraries and tools. The role involves significant customer interaction and on-site collaboration, requiring strong problem-solving and communication skills.

PhysicsX North Tyneside, NE29 8EP, United Kingdom
On-site Permanent Clearance Required
PhysicsX logo

Machine Learning Engineer

As a Senior Machine Learning Engineer, you will lead the deployment of AI models and engineering surrogates to customer production environments, working closely with Data Scientists, Simulation Engineers, and customers. You will drive technical decisions, mentor team members, and travel to customer sites to build solutions on-site, ensuring practical and impactful outcomes.

PhysicsX United Kingdom
Hybrid Permanent

Machine learning Engineer

Work as a Machine Learning Engineer building production-grade AI systems for clients in energy, sustainability, and public sectors. Focus on operationalising machine learning models, designing scalable infrastructure, and translating technical concepts for stakeholders. Collaborate across engineering, data science, and commercial teams to deliver real-world impact through responsible AI.

Faculty London, United Kingdom
Hybrid Permanent

Machine Learning Engineer

This role involves working within an R&D team to develop cutting-edge solutions in national security using generative AI and machine learning. You will collaborate with industry partners, have end-to-end project exposure, and benefit from a collaborative, autonomous environment with strong career development opportunities.

SF Partners Birmingham, West Midlands (county), United Kingdom £60,000 – £75,000 pa
Hybrid Permanent Clearance Required

Machine Learning Engineer

This role involves designing, building, and deploying advanced machine learning models to solve complex operational challenges in real-world environments. You will work closely with a high-calibre founding team, industrial data, and customer environments to take machine learning systems from early validation through to scalable deployment.

Platform Recruitment London, United Kingdom £60,000 – £70,000 pa
On-site Permanent

Machine Learning Engineer

Role:Machine Learning EngineerLocation: LincolnshireWorking Arrangement:HybridSalary:Up to £90kAre you an AI/ML Engineer who enjoys solving complex real-world problems where machine learning directly impacts operational outcomes?We're looking for an experienced engineer to join a specialist team developing next-generation AI and machine learning...

Rebel Recruitment Lincolnshire, United Kingdom £80,000 – £90,000 pa
Hiring locations

Where this role is hiring

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

FAQs

Common questions

  • Essential skills include programming (especially Python), mathematics, statistics, and a deep understanding of machine learning algorithms and frameworks.

  • Gain relevant skills through courses and projects, and consider internships or junior roles to build practical experience.

  • Responsibilities include developing and deploying machine learning models, collaborating with data scientists, and maintaining model performance.

  • Progression typically starts from Junior to Senior, then to Principal, with increasing responsibilities and leadership roles.

  • Salaries vary based on experience and location. For specific salary ranges, please refer to the salary section on this page.

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