Staff Machine Learning Engineer

Takeaway.com; Amsterdam; On-site; EUR 180,000 - 240,000; Full time

Job summary

Just Eat Takeaway.com is seeking a Staff Machine Learning Engineer to lead the ML infrastructure roadmap and architectural direction for a scalable, hybrid cloud platform powering personalised recommendations and intelligent targeting across multiple markets.

You will own the design of the ML platform, oversee GPU compute strategies, and drive the adoption of Generative AI while balancing performance, cost, and reliability.

Qualifications

• Experience defining and delivering technical roadmaps for large-scale ML platforms.
• Strong background in production ML architecture balancing latency, quality, and cost.
• Experience leading adoption of LLMs from experimentation to production deployment.

Responsibilities

• Own the technical roadmap for the ML infrastructure domain including GPU compute, model serving, training platforms and observability.
• Lead evolution of the foundation model platform in a hybrid AWS/GCP environment.
• Define GPU compute strategy across Kubernetes, Vertex AI and SageMaker.
• Drive production adoption of Generative AI and LLM capabilities with governance and evaluation practices.
• Collaborate with engineering teams to remove blockers and align ML systems with business priorities.
• Provide architectural guidance across multiple teams and mentor engineers to promote engineering excellence.

Skills

Technical roadmaps

ML infrastructure

LLMs / Generative AI

Kubernetes

Model serving

GCP / AWS / Vertex AI / SageMaker

Cross-team leadership

Tools

Kubernetes

Vertex AI

SageMaker

GCP

AWS

Job description

At Just Eat Takeaway.com, the AI Growth team builds the AI systems that make the marketplace more relevant for millions of customers and partners across 14 countries. From powering personalised recommendations and intelligent targeting to developing the foundation model platform, the team is shaping the future of AI at scale. As a Staff Machine Learning Engineer, you'll provide technical leadership for the ML infrastructure that underpins these capabilities, working across teams to define the architecture, roadmap and engineering direction for the next generation of the platform.

You’ll play a key role through strategic thinking and hands on technical leadership. Working closely with engineers, data scientists and platform teams, you’ll make decisions that enable innovation at scale while balancing performance, cost and reliability to deliver the best possible experience for customers.

These are some of the key components to the position:

• Own the technical roadmap for the ML infrastructure domain, defining priorities across GPU compute, model serving, training platforms and observability.
• Lead the evolution of the foundation model platform as the environment expands from GCP-first to a hybrid AWS and GCP architecture.
• Define GPU compute strategy across Kubernetes, Vertex AI and SageMaker, balancing performance, scalability and cost efficiency.
• Drive the production adoption of Generative AI and LLM capabilities, establishing best practices for evaluation, deployment, experimentation and governance.
• Collaborate with engineering teams to resolve cross-platform dependencies and remove technical blockers before they impact delivery.
• Provide technical leadership and architectural guidance across multiple teams, influencing engineering direction beyond your immediate domain.
• Partner with product, platform and infrastructure teams to ensure ML systems are reliable, scalable and aligned to business priorities.
• Raise the bar by improving platform observability, monitoring model performance, training efficiency and operational health across the ML ecosystem.
• Mentor engineers and promote engineering excellence through knowledge sharing, technical reviews and collaborative problem solving.
• Own architectural decisions that balance speed, scalability and long‑term maintainability while supporting AI growth strategy.

What will you bring to the team?

• Experience defining and delivering technical roadmaps for large-scale ML platforms, aligning engineering priorities with business goals.
• Strong understanding of production ML architecture, balancing latency, model quality, infrastructure cost and maintainability.
• Experience leading the adoption of LLMs or Generative AI from experimentation through to production deployment and operation.
• Deep knowledge of model serving architectures, with the ability to evaluate online, batch, synchronous and asynchronous serving strategies.
• Experience building or overseeing monitoring for multiple production ML models, including model drift, data quality and operational performance.
• Advanced Kubernetes knowledge, with the ability to troubleshoot cluster-level issues across security, networking, RBAC and platform operations.
• Strong collaboration and stakeholder management skills, influencing technical decisions across multiple engineering teams and business functions.
• Pragmatic problem‑solving mindset, balancing rapid delivery with long‑term platform scalability and engineering excellence.
• Experience optimising GPU infrastructure, cloud platforms or distributed ML workloads to improve efficiency and reduce operational costs.
• Passion for mentoring others, sharing knowledge and fostering a collaborative culture that helps teams deliver their best work.

At JET, this is how we play

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, helping one another to succeed and celebrating wins.