Senior Machine Learning Engineer
Senior Machine Learning Engineer

Senior Machine Learning Engineer

Full-Time 48000 - 72000 ÂŁ / year (est.) No home office possible
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At a Glance

  • Tasks: Lead the design of next-gen ML platforms and build scalable ML infrastructure.
  • Company: Join Policy Expert, a fast-growing insurance tech company transforming the industry.
  • Benefits: Hybrid work, competitive salary, learning budget, and private medical cover.
  • Why this job: Make a real impact in the insurance sector with cutting-edge machine learning technology.
  • Qualifications: 4+ years in ML systems, strong Python skills, and GCP experience required.
  • Other info: Inclusive workplace with a commitment to diversity and equal opportunities.

The predicted salary is between 48000 - 72000 ÂŁ per year.

Join to apply for the Senior Machine Learning Engineer role at Policy Expert. Are you ready to transform the insurance industry? Policy Expert is a forward‑thinking business that loves to get things done. Leveraging proprietary technology and smart data, we offer reliable products and a wow customer experience. Having achieved rapid growth since being founded in 2011, we've won over 1.5 million customers in Home, Motor and Pet insurance and have been ranked the UK's No.1‑rated home insurer by Review Centre since 2013.

Day to day

We are seeking a Senior Machine Learning Engineer to play a leading role in the design and evolution of Policy Expert's next‑generation ML platform on Google Cloud. You'll work as a hands‑on technical expert in building reusable, scalable, and observable ML infrastructure that empowers data scientists and product teams to deliver measurable business impact. This is a high‑impact individual contributor role for an engineer who enjoys coding, automation, and bringing order to complex DS/ML ecosystems.

  • Design, implement and standardise end‑to‑end machine learning pipelines using Vertex AI Pipelines, Model Registry, and Cloud Run, with a strong focus on reliability, automation, and cost efficiency.
  • Build reusable components and templates to accelerate model delivery across squads (training, evaluation, registry, monitoring).
  • Develop MLOps frameworks and SDKs around metadata tracking, feature versioning, model governance, and CI/CD integration (e.g., Cloud Build, Terraform, GitHub Actions).
  • Partner with data scientists and pricing analysts to translate model prototypes into fully automated, monitored deployments.
  • Optimise data processing and orchestration using BigQuery, Dataflow, and cloud‑native patterns (Container, Cloud Composer, Pub/Sub).
  • Support platform adoption by mentoring ML engineers and data scientists, and contributing to shared documentation, examples, and tooling.
  • Mentor and upskill peers in engineering excellence, code quality, and platform use.
  • Stay close to emerging trends in ML systems, generative AI, and agents; evaluating their fit within the MLOps landscape.

Who are you

  • A degree in Computer Science, Software Engineering, Data Science, or another quantitative field.
  • 4+ years of experience building and deploying production ML systems, with significant time spent on GCP.
  • Deep, hands‑on experience with Vertex AI (Pipelines, Model Registry, Experiments, Model Monitoring) and GCP services such as BigQuery, Cloud Storage, and Cloud Run.
  • Expert‑level Python engineering skills: writing clean, testable, modular code suitable for CI/CD environments.
  • Proven track record of designing MLOps or ML platform tooling, not just consuming it (e.g., custom pipeline components, SDKs, or frameworks).
  • Strong understanding of model lifecycle automation, including reproducibility, validation, drift detection, and rollback strategies.
  • Solid grasp of containerisation and infrastructure‑as‑code (Docker, Terraform, GCP IAM).
  • A collaborative, pragmatic mindset: equally comfortable discussing architecture with engineers and practical trade‑offs with data scientists.
  • Familiarity with neural network frameworks such as PyTorch or TensorFlow, and interest in GenAI or agentic workflows (LangChain, Vertex AI Agents, etc.) is a plus.
  • Knowledge of the insurance industry would be an advantage but not essential.

Benefits

  • This role will be based in our London office in a 50/50 Hybrid mode.
  • We match your pension contributions up to 7%.
  • Private medical & Dental cover.
  • Learning budget of ÂŁ1,000 a year + Study leave (with encouragement to use it).
  • Enhanced maternity & paternity.
  • Travel season ticket loan.
  • Access to a wide selection of London O2 events and use of a Private Lounge.
  • Employee Wellbeing Programme.
  • Prayer room in Office.

Equal Opportunity

We pride ourselves on being an equal opportunity employer. We treat all applications equally and recruit based solely on an individual’s skills, knowledge, and experience. The quality and growing diversity of our team is a testament to this commitment. At Policy Expert, we are committed to fostering an inclusive and supportive environment for all candidates. If you require any reasonable adjustments during the interview process to accommodate your needs, please do not hesitate to let us know. We are dedicated to ensuring every candidate has an equal opportunity to succeed and will work with you to provide the necessary support. We aim to be in touch within 14 working days of your application – you will be notified if successful or unsuccessful. Please be encouraged to apply even if you do not meet all the requirements.

Senior Machine Learning Engineer employer: Policy Expert

At Policy Expert, we pride ourselves on being a forward-thinking employer that champions innovation and collaboration. Our London office offers a vibrant work culture with a strong emphasis on employee growth, providing a generous learning budget and opportunities for mentorship. With competitive benefits including private medical cover and a commitment to diversity and inclusion, we create an environment where every team member can thrive and contribute to transforming the insurance industry.
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Contact Detail:

Policy Expert Recruiting Team

StudySmarter Expert Advice 🤫

We think this is how you could land Senior Machine Learning Engineer

✨Tip Number 1

Network like a pro! Reach out to current employees at Policy Expert on LinkedIn. A friendly chat can give you insider info and maybe even a referral, which can really boost your chances.

✨Tip Number 2

Prepare for the interview by brushing up on your ML knowledge and GCP skills. Be ready to discuss your past projects and how they relate to the role. We want to see your passion for machine learning shine through!

✨Tip Number 3

Showcase your coding skills! If you have a GitHub or portfolio, make sure to share it. Demonstrating your hands-on experience with Python and ML systems can set you apart from the crowd.

✨Tip Number 4

Don’t forget to follow up after your interview! A quick thank-you email can leave a lasting impression and shows your enthusiasm for the role. Plus, it keeps you on their radar as they make their decision.

We think you need these skills to ace Senior Machine Learning Engineer

Machine Learning Engineering
Google Cloud Platform (GCP)
Vertex AI
Python Programming
MLOps Frameworks
CI/CD Integration
BigQuery
Dataflow
Containerisation (Docker)
Infrastructure as Code (Terraform)
Model Lifecycle Automation
Neural Network Frameworks (PyTorch, TensorFlow)
Collaboration Skills
Technical Documentation

Some tips for your application 🫡

Tailor Your CV: Make sure your CV is tailored to the Senior Machine Learning Engineer role. Highlight your experience with GCP, Vertex AI, and any relevant projects that showcase your skills in building ML systems. We want to see how you can bring value to our team!

Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about transforming the insurance industry and how your background aligns with our mission at Policy Expert. Let us know what excites you about this role!

Showcase Your Projects: If you've worked on any interesting ML projects, make sure to include them in your application. We love seeing real-world applications of your skills, especially if they involve automation or MLOps frameworks. Share your successes with us!

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 the role. Plus, it gives you a chance to explore more about our company culture!

How to prepare for a job interview at Policy Expert

✨Know Your Tech Inside Out

Make sure you’re well-versed in the technologies mentioned in the job description, especially Google Cloud Platform and Vertex AI. Brush up on your Python skills and be ready to discuss how you've implemented MLOps frameworks or built ML systems in the past.

✨Showcase Your Problem-Solving Skills

Prepare to discuss specific challenges you've faced in previous roles and how you overcame them. Use examples that highlight your ability to design scalable ML infrastructure and automate processes, as this is crucial for the role.

✨Collaborate and Communicate

Since the role involves working closely with data scientists and product teams, be ready to demonstrate your collaborative mindset. Share experiences where you’ve successfully partnered with others to deliver impactful results, and don’t shy away from discussing trade-offs in technical decisions.

✨Stay Updated on Trends

Familiarise yourself with the latest trends in machine learning and generative AI. Be prepared to discuss how these trends could fit into the MLOps landscape at Policy Expert, showing your enthusiasm for continuous learning and innovation.

Senior Machine Learning Engineer
Policy Expert

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