Machine Learning Engineer

Position: Senior Machine Learning Engineer
Department: Growth & Customer Experience Engineering
Reporting to: Technology Manager
Location: Bristol/London
Hybrid role: 3 days a week from office & 2 days working from home
Full time / Part time role: Full time

Ready for a challenge?

Then Just Eat Takeaway.com might be the place for you. We're a leading global online food delivery platform, and our vision is to empower everyday convenience.

Whether it's a Friday-night feast, a post-gym poke bowl, or grabbing some groceries, our tech platform connects tens of millions of customers with hundreds of thousands of restaurant, grocery and convenience partners across the globe.

About this role

We are looking for a Senior Machine Learning Engineer to join a cross functional team, focussing on growing our product algorithmic recommendations within Just Eat Takeaway.com.

Your team will focus on evolving existing machine learning and AI capabilities across the platform, improving those capabilities, and innovating new ones for the future.

As a Senior Engineer you will drive our architecture, write highly scalable and testable code, mentor engineers and challenge our teams to strive for excellence. You will work closely with a large number of teams, both internal and external, with inner-sourced development our standard way of working.

Ownership is one of the core engineering principles in our organisation - we write it and we own it. Engineers are expected to take responsibility for their work from discovery to production, ensuring the ongoing reliability and stability of our systems.

This role offers you the opportunity to lead, deliver, and care for cutting-edge ML systems that impact millions of customers daily.

These are some of the key components to the position:

• Collaborate extensively with Data Scientists, Product Managers, and Backend Engineers to bridge the gap between model development and production systems
• Lead the architectural design of end-to-end ML systems, from data ingestion and training pipelines to real-time inference and monitoring infrastructure
• Transform innovative data science prototypes into robust, scalable, and secure production software, taking ownership of the 'path to production'
• Drive the adoption of MLOps best practices (CI/CD for ML, model versioning, feature stores) to accelerate the feedback loop for Data Scientists
• Effectively communicate the complexities of ML systems (e.g., latency vs. accuracy trade-offs) to technical and non-technical stakeholders
• Build and maintain a strong network across the Data and Engineering organizations to ensure ML systems integrate seamlessly with the wider platform