1,713 Senior Machine Learning Engineer jobs in the United Kingdom

Senior Machine Learning Engineer
3 Month Contract Initially with most probable extension
Location- London- Working Remotely

Are you a Senior ML Engineer with a passion for building large-scale, production-ready systems? We're looking for a talented professional to join a leading global digital entertainment platform. This is a fantastic opportunity to work on a 3-month contract with a high probability of extension.

The Role

We need a Senior ML Engineer to help us design, build, and deploy robust machine learning systems that power our platform. You'll be a key part of our team, tackling complex challenges and ensuring our models are scalable and reliable in a production environment.

• Production Experience : Proven track record of developing large-scale ML systems. We're particularly interested in candidates who have experience at large, international companies.
• Technical Skills : Strong proficiency in languages like Java, Scala, or Python .
• Deep Learning Frameworks : Hands-on experience with either PyTorch or TensorFlow .
• Experience : Previous experience working on distributed systems is a plus.

This is an urgent vacancy with deadline where the hiring manager is shortlisting for an interview immediately. Please apply with a copy of your CV or send it khushboo. pandey @ randstad. Co. uk

Randstad Technologies is acting as an Employment Business in relation to this vacancy.

Machine Learning Engineer

Salary: Up to £90k + Equity

Location: Fully Remote in UK

I am working with an exciting start-up in the UK, who are looking for a Machine Learning Engineer to join their growing team.

As a core early team member, you’ll design, optimize, and deploy models that make real-time insights possible, all while shaping the technical direction of a fast-moving startup.

What You’ll Own:

• Build and optimize ML models
• Turn research prototypes into production-ready systems.
• Collaborate across product, hardware, and data teams to tackle real-world challenges.
• Influence technical strategy and contribute to the startup’s growth and product roadmap.

What We’re Looking For:

• Strong Python and deep learning skills (PyTorch/TensorFlow).
• Experience with TinyML frameworks (TensorFlow Lite, TVM, ExecuTorch).
• Expertise in model optimization for constrained devices (quantization, pruning, compression).
• Ability to take ownership and thrive in a fast-paced, flexible startup environment.

This role does not offer sponsorship!

If you interested, please reach out to-

• Design, build, and deploy scalable machine learning models and systems.
• Develop and implement ML pipelines for data processing, feature engineering, and model training.
• Collaborate with data scientists and researchers to bring models from prototype to production.
• Optimize ML models for performance, efficiency, and scalability.
• Develop and maintain robust code for ML applications, adhering to software engineering best practices.
• Monitor and evaluate the performance of deployed ML models, implementing necessary improvements.
• Research and experiment with new ML techniques, tools, and technologies.
• Work closely with engineering teams to integrate ML solutions into existing products and platforms.
• Contribute to the architecture and design of the ML infrastructure.
• Document ML models, systems, and processes thoroughly.

• Master's or Ph.D. in Computer Science, Machine Learning, Statistics, or a related quantitative field.
• Proven experience in building and deploying machine learning models in a production environment.
• Strong programming skills in Python, including experience with ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Solid understanding of machine learning algorithms, statistical modeling, and data mining techniques.
• Experience with software development best practices, including version control (Git) and CI/CD.
• Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies.
• Excellent problem-solving and analytical skills.
• Strong communication and collaboration skills, with the ability to work effectively in a remote team.
• Experience with MLOps principles and practices is a plus.
• Ability to work independently and manage project timelines effectively.

• Develop, train, and deploy machine learning models using various algorithms and frameworks.
• Build and maintain scalable ML pipelines for data processing, model training, and inference.
• Collaborate with data scientists to productionize machine learning models.
• Implement and optimize ML algorithms for performance and efficiency.
• Work with large datasets and ensure data quality and integrity for ML applications.
• Develop and maintain ML infrastructure and tooling.
• Monitor and evaluate the performance of deployed ML models, making necessary adjustments.
• Contribute to the design and architecture of ML systems.
• Stay current with the latest advancements in machine learning and AI technologies.
• Troubleshoot and resolve issues related to ML model performance and deployment.
• Participate in code reviews and adhere to software engineering best practices.

• Master's or Ph.D. in Computer Science, Machine Learning, Statistics, or a related quantitative field.
• Proven experience (3+ years) in developing and deploying machine learning models in a production environment.
• Proficiency in programming languages such as Python and experience with ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch).
• Experience with cloud platforms (AWS, Azure, GCP) and MLOps practices.
• Strong understanding of data structures, algorithms, and software engineering principles.
• Experience with big data technologies (e.g., Spark, Hadoop) is a plus.
• Excellent problem-solving and analytical skills.
• Strong communication and teamwork abilities.
• Familiarity with containerization technologies like Docker and Kubernetes.

• Designing, building, and deploying machine learning models and algorithms.
• Developing and maintaining robust data pipelines for model training and inference.
• Collaborating with data scientists to translate research models into production-ready code.
• Implementing and optimizing machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Performing data preprocessing, feature engineering, and model evaluation.
• Monitoring and maintaining deployed machine learning models, ensuring performance and reliability.
• Working with cloud platforms (e.g., AWS, Azure, GCP) for model training and deployment.
• Writing clean, efficient, and well-documented code, primarily in Python.
• Troubleshooting and debugging issues related to model performance and infrastructure.
• Staying up-to-date with the latest advancements in machine learning and AI technologies.
• Contributing to the development of best practices for ML engineering.