Senior Machine Learning Engineer

Ready for a challenge?

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 .

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.

Location: Hybrid - 3 days a week from our office & 2 days working from home

Reporting to: Technology Manager

These are some of the key ingredients to the role: 

• 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.
• Lead projects, mentor peers, and advocate for engineering excellence within the data science domain.

What will you bring to the table?

• Proficiency in Python and a strong understanding of software engineering principles (OO design, patterns, testing) applied to Machine Learning.
• Demonstrable experience designing and operating ML systems in production (not just training models in notebooks), including familiarity with serving patterns (e.g., REST APIs, batch inference, event-driven).
• Experience with orchestration tools (e.g., Airflow, Dagster) and cloud-native ML platforms (e.g., AWS SageMaker, GCP Vertex AI).
• Ability to influence decision-making regarding infrastructure and tooling, balancing "build vs. buy" discussions.
• Strong knowledge of Infrastructure as Code (Terraform) and containerization (Docker/Kubernetes).
• Familiarity with data engineering fundamentals (SQL, distributed data processing) to debug and optimize data flows.
• A proactive mindset to automate manual processes and a passion for improving the developer experience for Data Scientists.

At JET, this is on the menu:

Our teams forge connections internally and work with some of the best-known brands on the planet, giving us truly international impact in a dynamic environment. 

Fun, fast-paced and supportive, the JET culture is about movement, growth and about celebrating every aspect of our JETers. Thanks to them we stay one step ahead of the competition.

Inclusion, Diversity & Belonging