Senior Software Engineer - AI Fintech foundation

About the Team

Hopper's Fintech Foundation team sits at the centre of the company's mission: reshaping how more than 100 million monthly active users experience travel. It owns the pricing engine that balances customer demand against financial risk across dozens of partners — including Capital One, Air Canada, and Spirit Airlines. As an ML engineer on this team, you will directly shape how Hopper prices ancillary products in real time and at scale.

About the Role

As a Software Engineer on the Fintech Foundation team, you will design, build, and evolve the machine learning systems that power Hopper's pricing engine. Your work will have direct impact on revenue, financial risk management, and the customer experience across the full partner portfolio.

You will work closely with the Data Science, Engineering, and Product teams to deliver pricing solutions that are reliable, scalable, and oriented toward commercial outcomes.

What would your day-to-day look like

• Design and implement automated, reusable training pipelines to ensure consistent, scalable model delivery across the partner portfolio
• Build ETL pipelines with thoughtful feature engineering to guarantee clean, reliable inputs for pricing models
• Develop and deploy real-time ML pricing solutions to production, owning the full path from model to live environment
• Monitor production systems for latency, drift, and training-serving skew, optimising continuously to maintain model integrity
• Run champion-challenger tests on pricing and product construction levers to surface improvements and respond to shifting market conditions
• Partner with data scientists, engineers, and product stakeholders to translate business needs into well-scoped technical solutions

An ideal candidate has

• 5+ years of experience in a similar role, ideally within production ML systems or large-scale pricing platforms
• Proficiency in Python, Scala, and SQL, applied to production ML systems rather than exploratory work
• Experience in data modelling, software architecture, and distributed data processing frameworks at scale
• Deep understanding of ML algorithms and when to apply them in the context of pricing, demand forecasting, or similar commercial domains
• Strong analytical instincts and attention to detail, with a track record of catching issues before they reach production
• A product mindset that keeps the customer outcome in view, even when the work is deeply technical
• Clear, collaborative communication skills that hold up across data science, engineering, and business stakeholder audiences