2,147 Machine Learning Engineer jobs in the United Kingdom

Job Title: Machine Learning Engineer

Location: London, UK (Hybrid – 2–3 days onsite per week)

Contract Type: Contract

Duration: 6–12 months (possibility of extension)

Start Date: ASAP

Overview

We are seeking an experienced Machine Learning Engineer to join our data science and AI engineering team on a contract basis in London. The ideal candidate will be responsible for designing, developing, and deploying machine learning models and scalable data pipelines that support advanced analytics and intelligent automation initiatives.

This role offers a hybrid work arrangement , combining flexibility with collaboration, and is ideal for a contractor who thrives in fast-paced, data-driven environments.

Key Responsibilities

• Design, build, and deploy machine learning models and AI-driven solutions to address business challenges.
• Collaborate with data scientists to transition prototypes into production-ready systems .
• Develop and maintain end-to-end ML pipelines for data ingestion, training, testing, and deployment.
• Optimise model performance, scalability, and reliability using MLOps best practices.
• Work with large-scale structured and unstructured datasets for model training and validation.
• Implement model monitoring, versioning, and retraining processes to ensure continuous improvement.
• Collaborate cross-functionally with engineering, data, and product teams to integrate ML solutions into production environments.
• Stay current with emerging trends in AI/ML technologies and contribute to innovation within the organisation.

Required Skills & Experience

• Proven experience (3–5+ years) as a Machine Learning Engineer , Data Scientist , or similar role.
• Strong programming skills in Python (experience with libraries such as TensorFlow, PyTorch, scikit-learn, pandas, NumPy).
• Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures .
• Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI).
• Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms.
• Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and orchestration.
• Knowledge of containerization and orchestration tools (Docker, Kubernetes).
• Experience integrating ML models into production environments via APIs or microservices.
• Excellent problem-solving, analytical, and communication skills.

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science , Data Science , Mathematics , or a related field.
• Familiarity with CI/CD pipelines for ML model deployment.
• Exposure to natural language processing (NLP) , computer vision , or reinforcement learning projects.
• Experience working in Agile/Scrum environments.

Contract Details

• Location: Hybrid – London (onsite 2–3 days per week)
• Type: Day-rate contract (Outside/Inside IR35 subject to assessment)
• Duration: 6–12 months (extension likely)
• Start Date: Immediate or within 2–4 weeks

Why Join

• Work with a talented, cross-functional AI and data engineering team.
• Contribute to cutting-edge ML solutions in a collaborative, innovation-driven environment.
• Hybrid flexibility with a strong London presence.

Job Title: Machine Learning Engineer

Location: London, UK (Hybrid – 2–3 days onsite per week)

Contract Type: Contract

Duration: 6–12 months (possibility of extension)

Start Date: ASAP

Overview

We are seeking an experienced Machine Learning Engineer to join our data science and AI engineering team on a contract basis in London. The ideal candidate will be responsible for designing, developing, and deploying machine learning models and scalable data pipelines that support advanced analytics and intelligent automation initiatives.

This role offers a hybrid work arrangement , combining flexibility with collaboration, and is ideal for a contractor who thrives in fast-paced, data-driven environments.

Key Responsibilities

• Design, build, and deploy machine learning models and AI-driven solutions to address business challenges.
• Collaborate with data scientists to transition prototypes into production-ready systems .
• Develop and maintain end-to-end ML pipelines for data ingestion, training, testing, and deployment.
• Optimise model performance, scalability, and reliability using MLOps best practices.
• Work with large-scale structured and unstructured datasets for model training and validation.
• Implement model monitoring, versioning, and retraining processes to ensure continuous improvement.
• Collaborate cross-functionally with engineering, data, and product teams to integrate ML solutions into production environments.
• Stay current with emerging trends in AI/ML technologies and contribute to innovation within the organisation.

Required Skills & Experience

• Proven experience (3–5+ years) as a Machine Learning Engineer , Data Scientist , or similar role.
• Strong programming skills in Python (experience with libraries such as TensorFlow, PyTorch, scikit-learn, pandas, NumPy).
• Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures .
• Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI).
• Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms.
• Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and orchestration.
• Knowledge of containerization and orchestration tools (Docker, Kubernetes).
• Experience integrating ML models into production environments via APIs or microservices.
• Excellent problem-solving, analytical, and communication skills.

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science , Data Science , Mathematics , or a related field.
• Familiarity with CI/CD pipelines for ML model deployment.
• Exposure to natural language processing (NLP) , computer vision , or reinforcement learning projects.
• Experience working in Agile/Scrum environments.

Contract Details

• Location: Hybrid – London (onsite 2–3 days per week)
• Type: Day-rate contract (Outside/Inside IR35 subject to assessment)
• Duration: 6–12 months (extension likely)
• Start Date: Immediate or within 2–4 weeks

Why Join

• Work with a talented, cross-functional AI and data engineering team.
• Contribute to cutting-edge ML solutions in a collaborative, innovation-driven environment.
• Hybrid flexibility with a strong London presence.

Machine Learning Engineer

We are working with a new start up company who are developing state-of-the-art medical devices to monitor health systems.

Experience Required:

• MSc or PhD in ML, Biomedical Engineering, or related field

• Strong experience with PyTorch/TensorFlow and Python

• Hands-on experience with TinyML or edge ML frameworks (TFLite, TVM, etc.)

• Skilled in model optimization for constrained devices

• Comfortable in a startup environment - proactive, collaborative, and adaptable

What you'll do:

• Build and optimize deep learning models for on-device biosignal monitoring

• Work on model compression, quantization, and other techniques for edge deployment

• Collaborate closely with the CTO and technical team to deploy models in production

• Contribute across the stack - from signal processing to product and hardware integration

• Help shape the direction of a fast-moving startup
  

We are seeking a highly skilled Machine Learning Engineer to join our team on a contract basis. You will be responsible for designing, building, and deploying machine learning models into production, working closely with data scientists, software engineers, and product teams to deliver scalable AI solutions. This is an excellent opportunity for someone who thrives in fast-paced environments and enjoys solving complex problems with real-world impact.

Key Responsibilities

• Develop, train, and optimize machine learning models for production use.
• Collaborate with data scientists to turn research prototypes into production-grade solutions.
• Build robust data pipelines and feature engineering workflows.
• Deploy ML solutions into cloud environments (AWS, GCP, or Azure).
• Implement monitoring, testing, and model performance evaluation frameworks.
• Work with engineering teams to ensure seamless integration of ML models into products.
• Contribute to improving infrastructure, tooling, and best practices for ML development and deployment.

Skills & Experience

Essential:

• Strong programming skills in Python (and frameworks such as PyTorch, TensorFlow, or Scikit-learn).
• Proven experience in developing and deploying machine learning models in production.
• Solid understanding of data structures, algorithms, and software engineering principles.
• Experience with ML pipelines and orchestration tools (e.g., Airflow, Kubeflow, MLflow).
• Proficiency in working with cloud services (AWS, GCP, or Azure).
• Strong understanding of CI/CD, containerisation (Docker), and orchestration (Kubernetes).
• Excellent problem-solving skills and ability to work independently in a fast-paced environment.

Desirable:

• Experience with NLP, computer vision, or time-series forecasting.
• Familiarity with distributed computing frameworks (Spark, Ray).
• Experience with MLOps and model governance practices.
• Previous contract experience in a similar ML engineering role.

Contract Details

• Duration: 6–12 months (extension possible)
• Location: London (Hybrid working model)
• Day Rate: Competitive, depending on experience

We are seeking a highly skilled Machine Learning Engineer to join our team on a contract basis. You will be responsible for designing, building, and deploying machine learning models into production, working closely with data scientists, software engineers, and product teams to deliver scalable AI solutions. This is an excellent opportunity for someone who thrives in fast-paced environments and enjoys solving complex problems with real-world impact.

Key Responsibilities

• Develop, train, and optimize machine learning models for production use.
• Collaborate with data scientists to turn research prototypes into production-grade solutions.
• Build robust data pipelines and feature engineering workflows.
• Deploy ML solutions into cloud environments (AWS, GCP, or Azure).
• Implement monitoring, testing, and model performance evaluation frameworks.
• Work with engineering teams to ensure seamless integration of ML models into products.
• Contribute to improving infrastructure, tooling, and best practices for ML development and deployment.

Skills & Experience

Essential:

• Strong programming skills in Python (and frameworks such as PyTorch, TensorFlow, or Scikit-learn).
• Proven experience in developing and deploying machine learning models in production.
• Solid understanding of data structures, algorithms, and software engineering principles.
• Experience with ML pipelines and orchestration tools (e.g., Airflow, Kubeflow, MLflow).
• Proficiency in working with cloud services (AWS, GCP, or Azure).
• Strong understanding of CI/CD, containerisation (Docker), and orchestration (Kubernetes).
• Excellent problem-solving skills and ability to work independently in a fast-paced environment.

Desirable:

• Experience with NLP, computer vision, or time-series forecasting.
• Familiarity with distributed computing frameworks (Spark, Ray).
• Experience with MLOps and model governance practices.
• Previous contract experience in a similar ML engineering role.

Contract Details

• Duration: 6–12 months (extension possible)
• Location: London (Hybrid working model)
• Day Rate: Competitive, depending on experience