Senior ML Engineer β€” Remote/Hybrid with Impact at Scale

Senior ML Engineer β€” Remote/Hybrid with Impact at Scale

Full-Time 70000 - 90000 Β£ / year (est.) No working from home possible
Wey Wey Web

At a Glance

  • Tasks: Lead ML practices and ensure production readiness of innovative features.
  • Company: Join Prima, a forward-thinking company transforming motor insurance.
  • Benefits: Enjoy remote/hybrid work, competitive salary, and professional growth opportunities.
  • Other info: Collaborative environment with a focus on mentorship and career advancement.
  • Why this job: Shape the future of motor insurance with cutting-edge machine learning technology.
  • Qualifications: Experience in machine learning and strong coding skills required.

The predicted salary is between 70000 - 90000 Β£ per year.

Prima is seeking a Senior Machine Learning Engineer to help shape the future of motor insurance. You will lead ML lifecycle practices, influence technology choices, and ensure production readiness of ML-driven features across Prima’s microservices landscape.

You will work with a large engineering team, advancing cloud-native ML tooling, and mentoring others while keeping a strong focus on code quality, design patterns, and robust delivery.

Senior ML Engineer β€” Remote/Hybrid with Impact at Scale employer: Wey Wey Web

At Prima, we pride ourselves on being an innovative employer that values curiosity and collaboration, making it an exciting place for a Machine Learning Engineer to thrive. With a strong focus on employee growth, we offer opportunities to work with cutting-edge technologies in a dynamic environment, all while contributing to a mission that impacts millions of drivers across the UK and Spain. Our inclusive work culture fosters creativity and experimentation, ensuring that every team member can make a meaningful impact in reshaping the future of motor insurance.

Wey Wey Web

Contact Details:

Wey Wey Web Recruitment Team

We think you need these skills to ace Senior ML Engineer β€” Remote/Hybrid with Impact at Scale

Machine Learning
Cloud-Native ML Tooling
ML Lifecycle Practices
Production Readiness
Microservices Architecture
Code Quality
Design Patterns