Senior ML Software Engineer Growth & Lifecycle Lounge by Zalando (all genders)

THE ROLE & THE TEAM

Lounge by Zalando is an online shopping club for fashion and lifestyle products serving millions of members across 20 European markets through daily time-limited sale campaigns. The Growth & Lifecycle team is the growth engine of Lounge turning anonymous traffic into registered members and one-time buyers into active high-lifetime-value customers. Personalisation is at the heart of that: deciding which campaigns each member sees and in which order across push email and on-site touchpoints.

Were looking for a Senior ML Software Engineer to own and grow the machine-learning systems behind this. Youll take our campaign-ordering personalisation from a single channel to many starting by bringing it to email via Braze building the feature pipelines training and evaluation workflows and scalable batch inference that make it work and proving the impact with rigorous A/B testing. Youll work in tandem with our Principal Engineers product managers and partner data-science teams and youll build the way our team builds: heavily leveraging AI coding agents to move fast without cutting corners.

Your mandate: put personalisation to work across every Lounge channel owning the feature training and inference pipelines that decide the right message for every member and proving the lift.

INCLUSIVE BY DESIGN

If you think you have what it takes we encourage you to apply even if you dont meet every single requirement. You may just be the right candidate for this or other roles!

At Zalando our vision is to be the leading pan-European ecosystem for fashion and lifestyle e-commerce one that thrives on diversity and is truly inclusive by design. We believe that diverse teams fuel innovation and creativity and we actively seek out talent from all backgrounds.

We actively seek to reduce bias in our hiring and employment processes focusing on your qualifications skills and contributions. To support this we kindly ask that you refrain from including personal details such as your photo age or marital status in your CV ensuring a fair and equitable evaluation based solely on your abilities and potential.

We are committed to providing an exceptional and accessible candidate experience for everyone. If you require any accommodations to support you throughout the hiring process please let us know we are here to assist you.

Discover more about our commitment to creating a diverse and inclusive workplace: WED LOVE YOU TO DO (AND LOVE DOING)

• Own personalisation end to end: Take our campaign-ordering ranking system from one channel to many taking ownership of a proven production model extending it to email via Braze and evolving it into a system our team fully owns and iterates on.

• Build feature and training pipelines at scale: Design and implement feature engineering in Spark/Databricks against our campaign and behavioural data backed by a feature store with the audits golden examples and parity checks that let you prove a rewritten pipeline matches the system it replaces.

• Run inference in production: Operate scalable batch (and where it fits real-time) inference serving millions of requests tuning for throughput and cost and integrating the ranked output into our lifecycle messaging so it reaches members at the right moment.

• Prove the impact: Build the tracking attribution and A/B testing that measure personalised vs. non-personalised outcomes and make incremental lift not vanity metrics the definition of success.

• Connect models to the growth engine: Wire data-science models (churn next-best-action propensity) into real customer touchpoints and help push our roadmap from copilots toward autonomous self-optimising marketing.

• Raise the bar and grow others: Design for testability and reliability lead production-readiness reviews for your area propose and implement technical standards and mentor mid-level and junior engineers as a senior technical voice on the team.

• Strong ML engineering experience: A solid track record productionising machine learning building and operating feature pipelines training workflows offline evaluation/backtesting and model serving not just prototyping in notebooks.

• Big-data fluency: Hands-on expertise with distributed data processing (Spark ideally on Databricks) and the feature engineering that recommendation ranking or personalisation systems depend on aggregations recency windows matching logic and metadata joins at scale.

• Production Python: Deep professional Python for ML systems; comfortable owning the full lifecycle from data to deployed inference. Exposure to a JVM language (our wider platform is Kotlin) is a plus.

• Cloud-native ML ops: Experience running ML on AWS (e.g. SageMaker or comparable serving) with Kubernetes CI/CD observability and a genuine ownership mindset you build it ship it and keep it healthy.

• Experimentation rigor: You measure what you build A/B testing at scale incrementality and offline/online evaluation you can defend.

• AI-native ways of working: You use AI coding tools and agent-assisted workflows as a core part of how you engineer its how our team moves from transpiling feature logic to accelerating validation.

• Senior collaboration: You explain complex technical concepts clearly to non-specialists drive cross-team projects resolve ambiguity and lift the engineers around you.

OUR OFFER

Zalando provides a range of benefits heres an overview of what you can expect. Ask your Talent Acquisition Partner to learn more about what we offer.

• Employee shares program

• 40% off fashion and beauty products sold and shipped by Zalando 30% off Lounge by Zalando discounts from external partners

• 2 paid volunteering days a year

• 27 days of vacation a year to start for full-time employees

• Relocation assistance available (subject to prior agreement)

• Family services including counseling and support

• Health and wellbeing options (including Wellhub formerly Gympass)

• Mental health support and coaching available

• Drive your development through our training platform and biannual peer-to-peer review