About this role

Our ML Platform Infrastructure team provides high speed intelligence for AI Systems across search, recommendations, personalization, forecasting, and emerging GenAI experiences. We focus on low-latency features, embeddings and real-time updates. We operate a feature store platform that we are scaling beyond just a feature store to become Zalando’s central discovery hub to serve customer, product and content understanding in real time. As a Software Engineer on our team, you will play a key role in building and scaling these capabilities, helping to unlock customer insights and predictive trending.

INCLUSIVE BY DESIGN

At Zalando, our vision is to be inclusive by design. And this vision starts with our hiring - we do not discriminate on the basis of gender identity, sexual orientation, personal expression, ethnicity, religious belief, or disability status. You are welcome to leave out your picture, age, or marital status from your application. We only assess candidates on their qualifications and merit.

We want to provide you with a great candidate experience. Feel free to inform us of any accommodations you may need, so we can best support you throughout the hiring process.

do.BETTER - our diversity & inclusion strategy: https://corporate.zalando.com/en/our-impact/dobetter-our-diversity-and-inclusion-strategy

Our employee resource groups: https://corporate.zalando.com/en/our-impact/our-employee-resource-groups

WHAT WE’D LOVE YOU TO DO (AND LOVE DOING)

Build & Enhance the Platform: Collaborate on the design, development, and maintenance of our core ML platform, including our feature store and real-time data services.
Improve Platform Reliability: Contribute to our platform's reliability by implementing monitoring, and alerting. You'll help track Service Level Objectives (SLOs) and participate in incident response and post-mortems to learn and improve our systems.
Implement Secure Solutions: Apply security best practices within the platform, including access management, secrets handling, and network configurations, following guidance from senior engineers.
Boost Developer Productivity: Enhance our CI/CD pipelines, automation scripts (IaC, GitOps), and self-service tooling. You'll also help improve our documentation to make it easier for scientists and engineers to use our platform.
Collaborate and Support: Work closely with data scientists and other engineers to understand their requirements, troubleshoot issues, and help them effectively use the ML platform for their projects.

WE’D LOVE TO MEET YOU IF YOU HAVE

Software Engineering Foundation: 2-4 years of professional software engineering experience, with a focus on building backend services, data infrastructure, or platform engineering. A keen interest in MLOps and a desire to grow in this domain is essential.
Cloud and Containers: Experience with a major cloud provider (AWS preferred) and hands-on experience with containerization and orchestration (Docker, Kubernetes).
Data Processing Interest: Familiarity with data processing concepts. Experience working with data schemas, data contracts, and streaming technologies (e.g., Kafka, Spark/Flink) is a strong plus.
Collaborative Mindset: Good communication skills and a strong desire to work in a team environment. You are eager to learn from senior engineers and share your own knowledge with peers.
Problem-Solving Skills: You are motivated to solve complex technical challenges and are passionate about building robust, scalable, and easy-to-use tools for other developers and data scientists.
Observability Awareness: An understanding of observability principles (metrics, logging, tracing) and a willingness to participate in an on-call rotation to support the systems you build.

Bonus/ Nice to have

ML Systems Exposure: Prior experience with feature stores (e.g., Feast, Hopsworks, SageMaker Feature Store) or model serving stacks (e.g., KServe, Triton, TorchServe).
Streaming and batch data for ML: Partner with customer teams to enable consistent online/offline features, robust data contracts, and schema/versioning practices that reduce drift and breakage.
Vector search and retrieval: Experience with similarity search and embeddings in production (e.g., FAISS, Milvus, OpenSearch KNN, pgvector) and their integration into ranking/retrieval workflows.
GenAI infrastructure: Experience with LLM serving, prompt management, or RAG architectures at scale.

If you think you have what it takes, we encourage you to apply even if you don't meet every single requirement. You may just be the right candidate for this or other roles.

OUR OFFER

Zalando provides a range of benefits, here’s an overview of what you can expect. Ask your Talent Acquisition Partner to learn more about what we offer. Learn all about Zalando and our values here: https://jobs.zalando.com/en/?gh_src=22377bdd1us

Employee shares program
40% off fashion and beauty products sold and shipped by Zalando, 30% off Zalando Lounge, discounts from external partners
2 paid volunteering days a year
Hybrid working model with 60% remote per week, actual practice is up to each team to best support their collaboration
Work from abroad for up to 30 working days a year
27 days of vacation a year to start
Relocation assistance available (subject to prior agreement)
Family services, including counselling and support
Health and wellbeing options (including Gympass)
Mental health support and coaching available

INCLUSIVE BY DESIGN

At Zalando, our vision is to be inclusive by design. This vision starts with our hiring - we do not discriminate on the basis of gender identity, sexual orientation, personal expression, ethnicity, religious belief, or disability status. You are welcome to leave out your picture, age, or marital status from your application. We only assess candidates on their qualifications and merit.

We want to provide you with a great candidate experience. Feel free to inform us of any accommodations you may need, so we can best support you throughout the hiring process.