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- Tasks: Design and implement innovative machine learning solutions for impactful projects.
- Company: Join a forward-thinking tech company in London with a collaborative culture.
- Benefits: Competitive salary, comprehensive benefits, and a chance to work with cutting-edge technologies.
- Other info: Dynamic environment with opportunities for career growth and innovation.
- Why this job: Make a real difference in the tech industry while working on exciting ML projects.
- Qualifications: Strong foundation in machine learning and hands-on experience with ML models.
The predicted salary is between 80000 - 100000 £ per year.
Join the analytics team as a Machine Learning Engineer in the technology industry, where you'll design and implement innovative machine learning solutions. This permanent role in London offers an exciting opportunity to work on impactful projects in a forward-thinking environment.
This opportunity is with a medium-sized organisation in the technology industry. The company is committed to utilising advanced analytics and machine learning to enhance its services and deliver value to its clients.
This role focuses on training custom models, building robust ML pipelines, and deploying systems at scale from research experimentation through to monitored production services.
- Design, train, and optimise machine learning models for audio processing tasks such as speaker diarization, automatic speech recognition (ASR), and voice activity detection.
- Build and maintain training and inference pipelines using PyTorch, and related ML frameworks.
- Source, curate, and prepare training datasets; implement preprocessing, augmentation, and validation workflows.
- Run structured experiments, evaluate model performance, and iterate based on measurable results.
- Build, deploy, and operate end-to-end MLOps pipelines, including experiment tracking, model versioning, and production monitoring.
- Package and deploy models using Docker and cloud infrastructure, with a focus on reliability and scalability.
- Design and deploy agent-based AI systems that can execute multi-step workflows and integrate with external tools.
- Build Model Context Protocol (MCP) servers to enable standardised integration between models, APIs, and data sources.
- Evaluate and integrate large language models into production systems where they add clear value.
- Collaborate with product and business teams to translate requirements into practical ML solutions.
A successful Machine Learning Engineer should have:
- Strong foundation in machine learning, deep learning, and optimisation.
- Hands-on experience training, evaluating, and deploying ML models in real-world systems.
- Proficiency with PyTorch (preferred) or TensorFlow; familiarity with the Hugging Face ecosystem.
- Experience with audio or speech models and frameworks.
- Experience building and maintaining end-to-end ML pipelines and MLOps tooling (e.g. MLflow, Weights & Biases, DVC, or similar).
- Strong Python skills; experience with Docker, CI/CD, and cloud platforms (Azure preferred).
- Practical experience designing agentic AI systems and integrating models with external services.
- Comfortable owning the full ML lifecycle, from data preparation to production deployment.
- Clear communicator who can work effectively across technical and non-technical teams.
Competitive salary ranging from £80,000 to £100,000 per annum. Comprehensive benefits package to support your well-being. Opportunity to work in a leading organisation within the insurance industry. Collaborative and innovative work environment in London. Chance to work on impactful projects using the latest technologies.
If you're a passionate Machine Learning Engineer looking to make a difference in the technology industry, we encourage you to apply and be part of this exciting opportunity in London.
Machine Learning Engineer - London employer: Michael Page Technology
Contact Detail:
Michael Page Technology Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer - London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with other Machine Learning Engineers. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects, especially those involving audio processing or MLOps. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice explaining your past projects and how you tackled challenges, as this will help you communicate effectively with both technical and non-technical teams.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got some fantastic opportunities waiting for you, and applying directly can sometimes give you an edge. Plus, it’s super easy to keep track of your applications!
We think you need these skills to ace Machine Learning Engineer - London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Machine Learning Engineer role. Highlight your experience with PyTorch, ML pipelines, and any relevant projects you've worked on. We want to see how your skills match what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about machine learning and how you can contribute to our team. Be sure to mention specific projects or experiences that relate to the job description.
Showcase Your Projects: If you've worked on any cool machine learning projects, make sure to include them in your application. Whether it's audio processing tasks or building MLOps pipelines, we love seeing practical examples of your work!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you're serious about joining our team!
How to prepare for a job interview at Michael Page Technology
✨Know Your ML Fundamentals
Brush up on your machine learning concepts, especially deep learning and optimisation techniques. Be ready to discuss how you've applied these in real-world scenarios, particularly with audio processing tasks like ASR or speaker diarization.
✨Showcase Your Technical Skills
Prepare to demonstrate your hands-on experience with PyTorch or TensorFlow. Bring examples of ML models you've trained and deployed, and be ready to talk about the end-to-end ML pipelines you've built, including any MLOps tools you've used.
✨Communicate Clearly
Practice explaining complex technical concepts in simple terms. You'll need to collaborate with both technical and non-technical teams, so being able to articulate your ideas clearly will set you apart.
✨Prepare for Practical Scenarios
Expect to tackle practical problems during the interview. Think about how you would approach building and deploying an ML model from scratch, including data preparation, model training, and deployment strategies using Docker and cloud platforms.