Software Engineer (Machine Learning)

TL;DR

Software Engineer (Machine Learning): Building and optimizing machine learning solutions for the insurance claims management platform with an accent on scalability, reliability, and performance. Focus on leveraging advanced data analytics and generative AI to revolutionize the claims lifecycle and deliver actionable intelligence.

Location: Based in Milan, Italy (Hybrid/Remote options available)

Company

A leading online motor insurance provider using data and technology to rethink the insurance experience for millions of drivers across Europe.

What you will do

• Leverage machine learning and advanced data analytics to identify risks and opportunities in the claims management platform.
• Analyze and interpret complex data to support the claims management lifecycle.
• Contribute to company growth through actionable insights derived from data.
• Collaborate with cross-functional teams to transform data into intelligence and intelligence into action.
• Write clean, maintainable, and production-ready code with a focus on scalability and performance.

Requirements

• Strong software engineering background with a focus on clean, production-ready code.
• Proficiency in Python and hands-on experience with Pandas, Numpy, PyTorch or Tensorflow, Hugging Face, and GenAI.
• Solid experience with RDBMS, specifically Postgres.
• Broad knowledge of machine learning paradigms, from classical models to deep learning and computer vision.
• Strong quantitative, logical, and analytical skills.
• Fluency in English is required.

Nice to have

• Experience with Elixir or Rust.
• Experience working in a Cloud-based environment, preferably AWS.
• Ability to create, manipulate, and analyze large datasets.

Culture & Benefits

• Full flexibility to work from home, the office, or a mix of both.
• Work from anywhere for up to 30 days per year.
• Access to learning resources, mentorship, and personalized growth plans.
• Private healthcare, gym discounts, and mental health support.
• Collaborative and experimentation-driven engineering culture.