Engineers who build, train, and deploy machine learning models. A core role in the tech industry, driving innovation and solving complex problems.
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.
A typical week for a Machine Learning Engineer is a mix of coding, model training, and collaboration with cross-functional teams.
Based on advertised midpoints across the 99 UK listings priced in pounds in the last 12 months. Base salary only.
% of 163 listings posted in the last 12 months that mention each skill, extracted from job descriptions.
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.
Assist in the development and testing of machine learning models, with a focus on learning and gaining hands-on experience.
Own the development and deployment of machine learning models, working closely with data scientists and software engineers.
Lead the design and implementation of complex machine learning systems, guiding junior team members and driving innovation.
Strategise and oversee the machine learning initiatives of an organisation, influencing the direction of projects and mentoring the team.
There's no single route, but most people follow some version of these steps.
Gain a strong foundation in programming, mathematics, and statistics. Familiarise yourself with key machine learning concepts and tools.
Apply your knowledge by working on real-world projects. This could be through internships, personal projects, or open-source contributions.
Start your career as a Junior Machine Learning Engineer, working on smaller projects and learning from more experienced colleagues.
Develop expertise in specific areas of machine learning, such as deep learning or reinforcement learning. Take on more complex projects and leadership roles.
As a Senior or Principal Machine Learning Engineer, lead major projects, mentor junior team members, and drive innovation within your organisation.
Delivery-focused role embedding physics-aware AI models into customer tools, building reliable ML data pipelines and working with 3D point-cloud and mesh data. Spans data science and software engineering, translating R&D into reusable libraries and products, with regular on-site customer travel.
TensorFlow · MLflow · Spark +6
Builds and deploys machine learning systems for industrial physics challenges, designing reliable data pipelines and working with 3D point-cloud and mesh data. Partners directly with customers on-site to embed AI models into practical tools, translating R&D outputs into reusable libraries and products. Requires Python, cloud, and MLOps experience with frequent travel.
TensorFlow · MLflow · Spark +6
Work across the full machine learning lifecycle, from concept and architecture through data collection, training, integration, testing and evaluation. Models are integrated with in-house electronics to produce prototype and demonstrator units, spanning computer vision, generative models and novel methods across multiple domains.
TensorFlow · PyTorch · CUDA +1
Faculty
Deliver bespoke, production-grade machine learning systems for clients across government, finance, retail, energy, life sciences and defence. The role spans building and deploying ML software and infrastructure, defining deployment standards, and leading technical scoping and architecture decisions while advising non-technical stakeholders. Work centres on Python, frameworks like Scikit-learn, TensorFlow or PyTorch, and cloud platforms with Docker and Kubernetes.
Scikit-learn · TensorFlow · PyTorch +5
Reperio Human Capital
Hands-on engineering role owning AI/ML initiatives end-to-end, from solution design and experimentation through deployment, monitoring and optimisation. Involves building production ML and GenAI solutions including RAG architectures, working with Snowflake and cloud data platforms, and applying MLOps, CI/CD and DevSecOps practices within cross-functional teams.
Snowflake · AWS · Linux +2
Faculty
Deliver bespoke, production-grade machine learning systems for government and national security clients, spanning the full ML lifecycle from scoping to deployment. Work with Python, Scikit-learn, TensorFlow or PyTorch, plus cloud platforms and Docker/Kubernetes, while advising stakeholders and setting standards for deploying ML at scale.
Scikit-learn · TensorFlow · PyTorch +5
C4S Search Ltd
Design, build and deploy machine learning models into production, with a focus on recommendation systems covering ranking and search optimisation. Work alongside data scientists, engineers and product stakeholders, maintaining MLOps pipelines and ensuring scalability of ML systems on Azure. Six-month contract within a hybrid North London team.
Azure · TensorFlow · PyTorch
ARM · 11–50 people · Bootstrapped
The OpportunityA leading financial consultancy is seeking a Senior AI & ML Engineer to lead the design, development and deployment of cutting-edge AI, Machine Learning and Generative AI solutions. You'll play a key role in shaping technical strategy, building production-grade…
The locations with the most live listings for this role today.
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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