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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
51
Salary range
£48k – £160k
Hiring companies
18
View 51 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

£48k – £160k

Based on advertised midpoints across the 78 priced listings posted in the last 12 months. Base salary only.

Salary visibility
4% of listings advertise a salary — up from 0% the year before.
By seniority
£k base
Mid
43
160
26 jobs
Senior
50
122
17 jobs
Lead
80
162
14 jobs
Skills & tools

What hiring managers ask for

% of 88 listings posted in the last 12 months that mention each skill, extracted from job descriptions.

Python
86%
Machine Learning
61%
PyTorch
43%
MLOps
36%
TensorFlow
35%
AWS
26%
Azure
26%
CI/CD
24%
SQL
20%
Kubernetes
20%
GCP
18%
Docker
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 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

51 live roles

See all 51 roles →
PhysicsX logo

Staff Machine Learning Software Engineer, Research

This role involves leading machine learning research initiatives in a high-performance computing environment, focusing on scalable AI models for multi-physics simulation in engineering and science. The engineer will bridge research and production by transforming prototypes into robust, distributed systems, mentoring junior staff, and shaping technical strategy. Work spans cloud and on-premise infrastructure with a strong emphasis on MLOps, software engineering best practices, and real-world deployment across advanced industries.

PhysicsX London, United Kingdom
On-site Permanent
PhysicsX logo

Senior Machine Learning Infrastructure Engineer, Research

This role involves designing and operating distributed training infrastructure for large physics models using NVIDIA DGX B200 systems, with a focus on optimizing training pipelines, data I/O performance, and model serving. The engineer will work closely with research scientists and ML engineers to enable efficient, scalable AI training and deployment in high-performance computing environments, while also building observability and reproducibility into the research workflow.

PhysicsX United Kingdom

Software Engineering Manager - Machine Learning- Systematic Quant Fund

This role involves leading a team of experienced engineers to develop and maintain core machine learning infrastructure, including distributed model training, LLM hosting, and scalable deployment systems. You will drive technical direction and integrate advanced ML capabilities into high-stakes systems within a deeply technical environment.

eFinancialCareers London, United Kingdom
On-site Permanent

Research Engineer, Machine Learning (Reinforcement Learning)

This role involves collaborating with researchers and engineers to advance the capabilities and safety of large language models through reinforcement learning. Responsibilities include developing core RL infrastructure, designing novel training environments, and optimizing performance across the stack.

Anthropic London, United Kingdom £260,000 – £630,000 pa
On-site Permanent

Research Engineer, Machine Learning (RL Velocity)

As a Research Engineer on the RL Velocity team, you will focus on building and improving the RL training infrastructure, identifying and removing bottlenecks, and partnering with researchers and engineering teams to enhance the efficiency and reliability of Anthropic's AI systems. This role involves high-leverage work that impacts the entire organization's ability to iterate and improve models quickly.

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

Principal Machine Learning Infrastructure Engineer

This role focuses on designing and operating scalable machine learning infrastructure for training and serving large physics-based models. You'll work closely with research scientists and ML engineers to optimize distributed training pipelines, improve data I/O performance, and build reliable model serving systems. The position emphasizes systems-level problem-solving, infrastructure automation, and enabling fast, reproducible experimentation on high-performance GPU clusters.

PhysicsX London, United Kingdom
See all 51 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
4 open

Edinburgh

Location
1 open

Oxford

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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