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- Tasks: Design and optimise machine learning models for audio processing and deploy robust ML pipelines.
- Company: Join a medium-sized insurance company leveraging advanced analytics and machine learning.
- Benefits: Competitive salary, comprehensive benefits, and a collaborative work environment.
- Why this job: Make a real impact in the insurance industry with cutting-edge AI technologies.
- Qualifications: Strong foundation in ML, experience with PyTorch, and proficiency in Python.
- Other info: Exciting opportunity for career growth in a dynamic London setting.
The predicted salary is between 80000 - 100000 £ per year.
Work on Cutting-Edge AI & Agentic Systems. End-to-End Ownership & Impact.
About Our Client
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.
Job Description
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.
The Successful Applicant
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.
What's on Offer
- 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 - London employer: Michael Page (UK)
Contact Detail:
Michael Page (UK) Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Machine Learning Engineer - London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. 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 common ML interview questions and be ready to discuss your past projects in detail. Confidence is key!
✨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 Senior Machine Learning Engineer - London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that match the job description. Highlight your hands-on experience with machine learning models, especially in audio processing tasks, as this is key for us.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about machine learning and how you can contribute to our team. Mention specific projects or technologies you've worked with that align with our needs.
Showcase Your Projects: If you've built any ML pipelines or agent-based AI systems, don’t hesitate to include them in your application. We love seeing real-world applications of your skills, so share links or descriptions of your work!
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’re considered for this exciting opportunity in London!
How to prepare for a job interview at Michael Page (UK)
✨Know Your Stuff
Make sure you brush up on your machine learning fundamentals, especially around audio processing tasks like speaker diarization and ASR. Be ready to discuss your hands-on experience with PyTorch and any relevant projects you've worked on.
✨Showcase Your Projects
Prepare to talk about specific ML models you've trained and deployed. Highlight the end-to-end pipelines you've built, including any MLOps tooling you've used. Real-world examples will make you stand out!
✨Communicate Clearly
Since you'll be collaborating with both technical and non-technical teams, practice explaining complex concepts in simple terms. This will show that you can bridge the gap between different stakeholders effectively.
✨Ask Insightful Questions
Prepare thoughtful questions about the company's use of AI and how they integrate machine learning into their services. This shows your genuine interest in the role and helps you understand if it's the right fit for you.
