Senior Machine Learning Engineer, Menu Personalization

About HelloFresh

At HelloFresh, we want to change the way people eat forever by offering our customers high-quality food and recipes for different meal occasions. Even after celebrating our 10-year anniversary, we continue to see this mission spread around the world and beyond our wildest dreams. Now, we are a global food solutions group and the world's leading meal kit company, active in 18 countries across 3 continents. So, how did we do it? Our weekly boxes full of exciting recipes and fresh ingredients have blossomed into a community of customers looking for delicious, healthy and sustainable options. The HelloFresh Group now includes our core brand, HelloFresh, as well as: Green Chef, EveryPlate, Chefs Plate, Factor_, YouFoodz, The Pets Table and GoodChop.

About the Team

Menu Personalization decides what millions of customers see when they open HelloFresh each week. The team owns the recommender systems that match customers to recipes across our global markets, and brings together Data Scientists, Backend Engineers, Data Engineers, ML Engineers, and Product to take ideas from experiment to production. The work directly shapes customer experience and business growth: when personalization gets better, customers find recipes they love faster, and HelloFresh becomes a stronger weekly habit.

At HelloFresh we are moving away from a model where software developers just execute tickets toward one where engineers are trusted to own customer problems. You take a problem, form a point of view, validate it with customers and data, and ship it using AI as a force multiplier.

About the Role

We are looking for a Senior Machine Learning (ML) Engineer for the Menu Personalization team, someone who helps build and operate the recommender stack that runs in production. You will design, build, and operate ML systems across feature pipelines, training workflows, model serving, experimentation tooling, and the infrastructure underneath, owning significant parts of the stack end to end. You will be expected to bring a point of view on how we improve personalization and to back it with data and user evidence. You will partner with Data Scientists to take models from notebook to production, with Data Engineers on features and pipelines, with Backend Engineers on online inference paths, and with Product on what to build next.

What you'll do

• Build and operate the ML systems behind menu personalization, working hands-on across 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.
• Own significant components of the recommender stack end to end, from design through deployment and ongoing operation.
• 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.
• Contribute to the personalization roadmap with Product and Engineering, backing your point of view with data and user evidence.
• Help raise the technical bar on the team through code and design reviews, mentorship, and the example you set on production ML craft.

What you'll bring

• 5+ years building and operating production ML systems.
• Production experience with recommender systems or large-scale personalization is a strong plus.
• Fluency across our data and ML stack (Python, Spark) and working knowledge of our 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.
• Hands-on experience with AI tooling (Claude Code, Cursor, Copilot) beyond casual experimentation; you use AI agents every day and have a practical sense of how the context you provide shapes output quality.
• Product sense: opinionated about what should be built and why, with the ability to back it with evidence and translate it into business value.
• A bias to ship; you take full ownership and finish the last twenty percent.

Let’s cut to the cheese, this is why you'll love it here

• Box Discount - Amazing discounts on 1 box per week! 75% discount on weekly HelloFresh and Chefs Plate meal kits AND 50% off weekly Factor meal box.
• Health & Wellness - Health & Dental benefits from day 1, a Health Spending Account, unlimited access to the Headspace app to meet your self-care needs, and 25% discount on GoodLife fitness memberships!
• Vacation & PTO - Time off is also an important part of self-care! We offer generous vacation and PTO to help you create a good work-life balance.
• Family Benefits - A parental leave top-up program for expectant parents.
• Growth & Development - We support your career progression and invest in your continued learning through experiences and initiatives owned by our dedicated L&D team
• Work Hard & Have Fun - From team socials to engaging company days, you’ll have plenty of opportunity to experience the fun!
• Diversity & Inclusion Initiatives - With impactful ERG’s like FreshPride, Women Empowered and LIMES, we are committed to our diversity, equity & inclusion efforts.
• Food Puns - this one is kind of a big dill if you haven’t already noticed. We even have some punny meeting room names!

Flexible Hybrid Approach

At HelloFresh, we know that flexible work arrangements are essential in enabling you to do your best work, while balancing your personal and life needs. Offering remote work flexibility, along with the opportunity to interact and collaborate in the office are all a part of creating a great employee experience.

To meet these needs, we are pleased to provide Flexible Hybrid work. Flexible Hybrid is a people-first approach that is based on choice, trust, personalization, and empowers teams to choose when and how often they work from the office and work from home, in addition to team days and company days. This means a minimum of 2 days in office per week, with most teams in office between 2-3 days a week.

#LI-HYBRID

HelloFresh Canada uses AI-integrated technology to help us process and evaluate applications more efficiently. This includes tools that screen and assess candidate qualifications based on the requirements for this role. While these tools assist our workflow, all final selection decisions are made by our hiring team.

This is a posting for an existing vacancy. We are actively seeking to fill this position.