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

ROLE 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. We're looking for a Senior ML Software Engineer to own and grow the machine-learning systems behind this. You'll 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. RESPONSIBILITIES - 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 audits, golden examples, and parity checks to 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 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, propose and implement technical standards, and mentor mid-level and junior engineers as a senior technical voice on the team. QUALIFICATIONS - 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-upa 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 fro& ML systems; comfortable owning the full lifecycle from data to deployed inference. A JVM language (Kotlin) exposure is a plus. - Cloud-native ML ops: experience running ML on AWS (e.g. SageMaker), with Kubernetes, CI/CD, observability, and an 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 your engineering. - Senior collaboration: you explain complex technical concepts to non-specialists, scope cross-team projects, resolve ambiguity, and lift the developers around you. WHAT WE OFFER Salary, equity, annual bonuses, and a performance-embedded culture. Zalando provides a wide range of benefits including: employee shares program, 40% off fashion and beauty products sold and shipped by Zalando, 30% off Lounge by Zalando, additional partner discounts, 2 paid volunteering days a year, 27 days of vacation, relocation assistance, family services and counseling, health & wellbeing including Wellhub, mental health support and coaching, and a dedicated learning and development budget.