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- Tasks: Build and run impactful ML systems, collaborating with Data Scientists and engineering teams.
- Company: Join a forward-thinking company that values innovation and teamwork.
- Benefits: Enjoy competitive salary, hybrid working, flexible hours, and generous annual leave.
- Other info: Dynamic environment with opportunities for growth and learning.
- Why this job: Make a real impact in the tech world while advancing your career in ML.
- Qualifications: Experience with ML platforms, Python, and strong collaboration skills.
The predicted salary is between 50000 - 70000 £ per year.
We’re looking for a Machine Learning Engineer to help build and run production‑ready ML systems that make a real impact across the business. You’ll work closely with Data Scientists and engineering teams, shaping the ML roadmap, developing scalable solutions, and driving innovation while growing your career.
You’ll Make An Impact By:
- Build and automate ML pipelines for feature engineering, model training, and model scoring using Python, PySpark, Databricks, and MLflow.
- Productionise Data Science models, converting notebooks into modular, tested, production‑ready code.
- Deploy models into batch and real‑time environments, managing versioning, promotion, rollback, and scheduled workflows via MLflow and APIs.
- Implement monitoring and observability, including data and model drift detection, performance alerts, logging, and automated retraining.
- Collaborate with Data Engineering and Platform teams on CI/CD integration, pipeline performance, compute optimisation, and secure deployment patterns.
- Maintain engineering standards, ensuring high‑quality testing, documentation, code quality, reproducibility, and operational reliability.
Your Skills And Experience:
- Experience with ML platforms including Databricks, MLflow, Delta Lake, and cloud environments.
- Proficient in Python, PySpark, and SQL, following production coding best practices.
- Understanding of data, distributed ML pipelines, and model deployment patterns, including monitoring, drift detection, and lifecycle operations.
- Exposure to CI/CD, containerisation, and API integration, with the ability to build scalable, production‑ready ML systems.
- Comfortable working technically while communicating effectively with Data Scientists, stakeholders, and cross‑functional teams.
Why You’ll Love It Here:
- Annual discretionary bonus
- Up to 11% pension contributions
- Hybrid working + flexible hours
- 25 days annual leave
Machine Learning Engineer in London employer: Intact Insurance UK
Contact Detail:
Intact Insurance UK Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in 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 ML projects, especially those involving Python, PySpark, and Databricks. 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 soft skills. Be ready to discuss your experience with CI/CD, model deployment, and how you’ve collaborated with cross-functional teams. Practice makes perfect!
✨Tip Number 4
Don’t forget to apply through our website! We love seeing applications come directly from candidates who are excited about joining us. Plus, it gives you a better chance to stand out in the hiring process.
We think you need these skills to ace Machine Learning Engineer in 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 Python, PySpark, and any ML platforms like Databricks or MLflow. We want to see how your skills align with what we’re looking for!
Showcase Your Projects: Include specific projects where you've built or deployed ML systems. Talk about the impact they had and the technologies you used. This helps us understand your hands-on experience and how you can contribute to our team.
Craft a Compelling Cover Letter: Your cover letter should tell us why you're excited about this role at StudySmarter. Share your passion for machine learning and how you envision making an impact here. Keep it engaging and personal!
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. Plus, we love seeing applications come in through our own platform!
How to prepare for a job interview at Intact Insurance UK
✨Know Your Tech Stack
Make sure you’re well-versed in the tools mentioned in the job description, like Python, PySpark, and MLflow. Brush up on your experience with Databricks and Delta Lake, as these will likely come up during technical discussions.
✨Showcase Your Projects
Prepare to discuss specific projects where you've built or deployed ML systems. Highlight your role in automating ML pipelines and how you tackled challenges like model drift detection or performance monitoring.
✨Collaboration is Key
Since you'll be working closely with Data Scientists and engineering teams, be ready to share examples of how you've successfully collaborated in the past. Emphasise your communication skills and how you bridge the gap between technical and non-technical stakeholders.
✨Ask Insightful Questions
Prepare thoughtful questions about the company’s ML roadmap and how they approach CI/CD integration. This shows your genuine interest in the role and helps you gauge if the company aligns with your career goals.