At a Glance
- Tasks: Design and optimise machine learning models for audio processing and deploy robust ML pipelines.
- Company: Join a medium-sized software development firm leading in AI and advanced analytics.
- Benefits: Competitive salary, comprehensive benefits, and a collaborative work environment.
- Other info: Exciting opportunity for professional growth in the heart of London.
- Why this job: Make a real impact with cutting-edge technology in a dynamic team.
- Qualifications: Strong foundation in machine learning and experience with PyTorch or TensorFlow.
The predicted salary is between 95000 - 95000 £ per year.
This opportunity is with a medium-sized software development organisation. 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 75,000 to 95,000 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 sector, we encourage you to apply and be part of this exciting opportunity in London.
Machine Learning - Engineer - London employer: Michael Page International
Michael Page is an excellent employer, offering a dynamic work environment in Leeds that fosters professional growth and development. With a strong focus on mentoring and team collaboration, employees benefit from hybrid working arrangements and a competitive salary package, including performance-based bonuses. Joining this multinational organisation means being part of a high-growth team dedicated to delivering exceptional tax compliance services across diverse sectors.
Contact Details:
Michael Page International Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning - Engineer - London
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at Michael Page International or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to Michael Page International.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like Michael Page International.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like Michael Page International that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace Machine Learning - Engineer - London
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Michael Page International.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Michael Page International and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at Michael Page International
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Michael Page International uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
