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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
18
Salary range
£35k – £100k
Hiring companies
5
View 18 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

£35k – £100k

Based on advertised midpoints across the 3 priced listings posted in the last 12 months. Base salary only.

By seniority
£k base
Mid
35
100
3 jobs
Skills & tools

What hiring managers ask for

% of 10 listings posted in the last 12 months that mention each skill, extracted from job descriptions.

PyTorch
90%
Python
70%
Machine Learning
70%
TensorFlow
60%
AWS
40%
GCP
40%
Kubernetes
40%
Pandas
30%
fastAPI
30%
Scipy
30%
Kubeflow
30%
Data Pipelines
30%
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

18 live roles

See all 18 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

As a Machine Learning Engineer, you will work on delivering bespoke AI solutions for diverse clients, focusing on the defence sector. You will collaborate with cross-functional teams to build and deploy production-grade ML systems, define best practices, and ensure technical feasibility. The role involves both on-site work with clients and remote flexibility.

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
Darktrace logo

Machine Learning Integration Engineer

As a Machine Learning Integration Engineer, you will work on deploying and optimizing machine learning models to enhance Darktrace’s cybersecurity services. You will collaborate with a cross-functional team to develop innovative solutions, optimize existing models, and contribute to rapid prototyping and project-based development.

Darktrace Cambridge, CB2 3BJ, United Kingdom
Hybrid Permanent
Luminance logo

Machine Learning Research Engineer

This is a fantastic opportunity to join market-leading UK AI company, Luminance. Named in Tech Nation’s prestigious Future Fifty list and the recipient of two Queen’s Awards, Luminance is the world’s most advanced AI technology which is disrupting the legal...

Luminance Cambridge, United Kingdom
Hybrid Permanent
NVIDIA logo

Senior Machine Learning Applications and Compiler Engineer, LPX

You will develop and optimize algorithms for NVIDIA's LPX inference and compiler stack, focusing on high-performance runtime and compiler components. Your work will involve benchmarking, profiling, and collaborating with hardware teams to influence future architectures and improve performance.

NVIDIA Cambridge, United Kingdom
Hybrid Permanent
NVIDIA logo

Senior Machine Learning Applications and Compiler Engineer, LPX

You will develop and optimize algorithms for NVIDIA's LPX inference and compiler stack, working at the intersection of large-scale systems, compilers, and deep learning. Responsibilities include building high-performance runtime and compiler components, benchmarking performance, and collaborating with hardware and software teams to influence future architectures.

NVIDIA
Hybrid Permanent
See all 18 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
10 open

Cambridge

Location
2 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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