Senior Staff Machine Learning Engineer, Menu Personalisation (m,f,x)

About the role

We are looking for a technical leader for the Menu Personalization ML systems, someone who owns the recommender stack that runs in production. You will set the direction for how we design, build, and operate the ML systems behind menu personalization, while staying hands-on across feature pipelines, training workflows, model serving, experimentation tooling, and the infrastructure underneath.

What you'll do

• Set the technical direction for the ML systems behind menu personalization, owning the end-to-end stack: feature pipelines, training workflows, model serving, experimentation tooling, and infrastructure.
• Take research and experiments to reliable production systems, partnering with Data Scientists on services that meet real latency, scalability, and observability requirements.
• Shape the personalization roadmap with Product and Engineering leadership, backing your point of view with data and user evidence.
• Operate what you build, instrumenting and improving your systems in production because shipping is the beginning of the learning cycle, not the end of it.
• Raise the technical bar across the team through architecture reviews, mentorship, and the example you set on production ML craft.
• Shape long-term architecture and make platform decisions whose payoff plays out over years rather than quarters.
• Drive engineering excellence beyond Menu Personalization by setting standards other ML and data teams adopt.

What you'll need

• 8+ years building and operating production ML systems, with a track record of technical leadership at scale.
• Architectural decisions in your past that held up over multiple years and influenced teams beyond your own.
• Production experience with recommender systems or large-scale personalization is a strong plus.
• Fluency across data and ML stack (Python, Spark) and backend and platform stack (Go, Kafka, Kubernetes), with hands-on experience across pipelines, model serving, and observability at scale.
• Statistical literacy to design honest experiments and the judgment to know when a model is actually better versus when the metrics are lying to you.
• Operational judgment to diagnose system misbehavior, find root causes, and ship systems you can debug under real load.
• Product sense: opinionated about what should be built and why, with the ability to back it with evidence and translate it into business value.

What they offer

• Compensation heavily weighted toward equity through Virtual Stock Option (VSO) and Restricted Stock Unit (RSU) plans.
• Flexible Hybrid work arrangement.
• Discount on HelloFresh meal kits.