Machine learning engineer job description.
Looking to hire a machine learning engineer or move into the role yourself? This machine learning engineer job description covers model training, testing, deployment, and automation of workflows — plus the technical skills and salary you can expect in this role.
What does a machine learning engineer do?
A machine learning engineer develops, tests, and deploys models that allow systems to learn from data and make predictions. They work closely with data scientists, software engineers, and product teams to integrate ML into real-world applications.
Responsibilities include cleaning data, feature engineering, model selection, evaluation, and deploying models to production using tools like Python, Scikit-learn, TensorFlow, or PyTorch. They also monitor model performance post-deployment.
In smaller teams, ML engineers may handle everything from data pipelines to APIs. In larger organisations, they specialise in model deployment, MLOps, or performance optimisation at scale.
Key responsibilities of a machine learning engineer.
ML engineers design, build, and deploy machine learning systems. Their responsibilities include:
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Developing training pipelines and evaluating model performance
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Collaborating with data scientists to productionise models
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Building APIs and services for real-time or batch inference
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Monitoring model drift and implementing retraining strategies
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Handling data preprocessing, feature engineering, and pipeline automation
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Integrating models with applications and analytics platforms
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Working with DevOps to deploy and scale ML infrastructure
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Maintaining version control of models and experiments
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Ensuring reproducibility and model explainability
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Documenting workflows, parameters, and model artefacts
This role blends data science, engineering, and production reliability.
Skills and requirements for a machine learning engineer.
ML engineers build and optimise models powering intelligent systems. Employers typically look for:
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3–6 years of experience in machine learning or AI engineering
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Strong Python skills, experience with scikit-learn, TensorFlow, or PyTorch
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Skilled in model design, training, tuning, and evaluation
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Familiarity with MLOps, deployment pipelines, and APIs
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Ability to collaborate with data scientists and developers
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Experience with classification, regression, or recommendation systems
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Strong statistical knowledge and data quality understanding
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Confidence managing models in production
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Comfortable adapting to new tools and frameworks
Most ML engineers bridge data science and engineering, ensuring model reliability.
Average salary for a machine learning engineer.
In the UK, the average salary for a machine learning engineer typically ranges from £45,000 to £80,000, depending on pipeline automation, model building, and performance tuning.
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Mid-level ML engineers typically earn between £45,000 and £60,000
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Senior engineers optimising large-scale production models often earn between £61,000 and £80,000
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Specialists in reinforcement learning or deep learning tend to receive premium pay
Salaries are highest in AI labs, fintech, and data-centric SaaS platforms.
Career progression for a machine learning engineer.
A machine learning engineer designs, trains, and deploys models to solve business problems using data. This role bridges data science and software engineering and is highly sought after. A typical path includes:
Data scientist / Junior ML engineer
Prepares datasets, selects algorithms, and tunes hyperparameters.
Machine learning engineer
Builds robust pipelines, deploys models, and ensures accuracy and reproducibility.
Senior ML engineer
Leads experimentation and supports live ML systems with MLOps best practices.
ML architect
Designs platform-level ML infrastructure and manages team delivery.
Director of ML / Head of AI
Leads model strategy, innovation, and performance measurement across the business.
salary guide
Our UK data salary guide.
Machine learning engineers develop, deploy, and monitor models in production environments. Salary should reflect technical delivery and cross-functional collaboration.
Our UK data salary guide includes ML salary benchmarks, 2024 comparisons, and projections through to 2026.
FAQS
Machine learning engineer FAQs.
Machine learning engineers focus on model integration, deployment, and monitoring. While data scientists test and explore, ML engineers build pipelines, manage model drift, and handle model rollback or failure scenarios.
Overemphasising academic ML knowledge without asking about real-world production experience. A strong ML engineer understands versioning, serving latency, and how to handle live data inconsistencies.
Experience with MLOps stacks (e.g. MLflow, SageMaker, Vertex AI), infrastructure awareness, and ability to collaborate with DevOps teams. Businesses want stability and reliability, not just accuracy.
Common paths include senior ML engineer, ML architect, or MLOps lead. Others shift into hybrid roles combining ML and backend or product responsibilities.
