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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
49
Salary range
£45k – £146k
Hiring companies
24
View 49 live roles → Get an alert

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

£45k – £146k

Based on advertised midpoints across the 65 priced listings posted in the last 12 months. Base salary only.

Salary visibility
2% of listings advertise a salary — up from 0% the year before.
By seniority
£k base
Mid
45
160
24 jobs
Senior
50
124
14 jobs
Lead
80
168
13 jobs
Skills & tools

What hiring managers ask for

% of 82 listings posted in the last 12 months that mention each skill, extracted from job descriptions.

Python
87%
Machine Learning
62%
PyTorch
48%
TensorFlow
37%
MLOps
37%
AWS
27%
Azure
26%
Kubernetes
24%
CI/CD
23%
Docker
21%
SQL
20%
GCP
20%
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

49 live roles

See all 49 roles →
PhysicsX logo

Staff Machine Learning Software Engineer, Research

Lead and shape research initiatives in machine learning for physics-based simulation, focusing on scalable and distributed training of foundation models. Translate research prototypes into robust, production-grade systems using cloud and HPC infrastructure, while mentoring junior engineers and guiding technical direction across the Research team.

PhysicsX London, United Kingdom
On-site Permanent
PhysicsX logo

Senior Machine Learning Infrastructure Engineer, Research

This role involves building and operating scalable ML infrastructure for training and serving large physics models, with a focus on distributed training optimization, data pipeline performance, and model deployment. You'll work closely with research scientists and ML engineers in a high-impact R&D environment, solving systems-level challenges in GPU clusters and HPC workflows. The position emphasizes end-to-end ownership of research infrastructure, reproducibility, and developer experience for fast iteration.

PhysicsX United Kingdom

Research Engineer, Machine Learning (RL Velocity)

This role involves building and optimizing the machine learning infrastructure that supports Anthropic’s reinforcement learning research. You'll work closely with researchers to improve training efficiency, debug performance bottlenecks, and develop tooling that accelerates model development. The position focuses on high-leverage platform improvements that scale across the entire research team.

Anthropic London, United Kingdom £370,000 – £630,000 pa
PhysicsX logo

Principal Machine Learning Infrastructure Engineer

This role involves designing and operating distributed machine learning infrastructure for large-scale physics-based AI models, with a focus on training efficiency, data pipeline performance, and model serving. The engineer will work closely with research scientists and ML engineers to build scalable systems on NVIDIA DGX hardware, optimize I/O for complex mesh datasets, and enable reliable deployment of models into customer environments. A strong foundation in distributed training, HPC systems, and Kubernetes is essential.

PhysicsX London, United Kingdom
See all 49 roles →
Top hirers

Companies hiring machine learning engineers

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Hiring locations

Where this role is hiring

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

London

Location
27 open

Cambridge

Location
5 open

Oxford

Location
1 open
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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