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- Tasks: Design and implement innovative machine learning solutions in the insurance industry.
- Company: Join a forward-thinking medium-sized organisation in London.
- Benefits: Competitive salary, comprehensive benefits, and a collaborative work environment.
- Why this job: Make a real impact with cutting-edge technology on exciting projects.
- Qualifications: Strong foundation in ML, experience with PyTorch, and Python skills required.
- Other info: Opportunity for career growth in a dynamic and innovative setting.
Join the analytics team as a Machine Learning Engineer in the insurance 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 insurance 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 insurance industry, we encourage you to apply and be part of this exciting opportunity in London.
Senior Machine Learning Engineer in London employer: Michael Page
Contact Detail:
Michael Page Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or attend meetups. 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.
✨Tip Number 3
Prepare for interviews by brushing up on common ML concepts and frameworks like PyTorch and Docker. Practice explaining your past projects and how you tackled challenges—this will help you stand out!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Senior Machine Learning Engineer in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with machine learning, especially in audio processing tasks. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects!
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. Keep it concise but impactful – we love a good story!
Showcase Your Projects: If you've worked on any cool ML projects, make sure to mention them! Whether it's building pipelines or deploying models, we want to know what you've done and how it relates to the role.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates from our team!
How to prepare for a job interview at Michael Page
✨Know Your ML Fundamentals
Brush up on your machine learning concepts, especially those related to audio processing tasks. Be ready to discuss how you’ve designed, trained, and optimised models in the past, as well as any specific frameworks like PyTorch or TensorFlow you've used.
✨Showcase Your Pipeline Experience
Prepare to talk about your experience with building and maintaining end-to-end ML pipelines. Highlight any tools you've used for MLOps, such as MLflow or Weights & Biases, and be ready to explain how you ensure reliability and scalability in your deployments.
✨Demonstrate Problem-Solving Skills
Think of examples where you've run structured experiments and iterated based on measurable results. Be prepared to discuss how you evaluate model performance and what steps you take when things don’t go as planned.
✨Communicate Clearly
Since collaboration is key, practice explaining complex technical concepts in simple terms. Be ready to share how you've worked with both technical and non-technical teams to translate requirements into practical ML solutions.
