Senior Software Engineer - Machine Learning Platform (all genders)

Reposted 11 Days Ago
Be an Early Applicant
Berlin
In-Office
Mid level
eCommerce • Retail
The Role
As a Software Engineer, you will build and enhance ML platforms, improve reliability, implement security practices, and boost developer productivity while collaborating with teams.
Summary Generated by Built In

Our ML Platform team builds the foundational intelligence layer that powers Zalando’s AI-native experiences. We provide low-latency features, embeddings, and real-time updates that enable applied science and product teams to deliver search, recommendations, personalization, forecasting, and emerging GenAI use cases. Today, we operate Zalando’s central Feature Store and are scaling it into a broader discovery hub for customer, product, and content understanding in real time..

As a Senior Software Engineer (ML Platform), you will play a key role in designing, building, and scaling these core ML infrastructure services. You’ll work hands-on with distributed systems, streaming pipelines, and feature platforms, while also mentoring peers and contributing to engineering best practices across the team.

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)

  • ML Subject Matter Expertise: Own the design and implementation of golden paths for building scalable, real-time features and embedding systems. Bring strong technical judgment to ensure our foundations are reliable and reusable.
  • Platform reliability: Deliver and maintain SLOs for feature freshness, data quality, and online/offline consistency; implement monitoring and safe deployments practices.
  • Embed security by design: Implement identity and access management, secrets management, network isolation, and data governance built in from the start to ensure compliance and trustworthiness by default..
  • Enable developer productivity: Drive automation and self-service (IaC, GitOps, CI/CD), Deliver documentation and onboarding assets that reduce time-to-first-success for applied scientists and engineers.
  • Provide technical guidance: Act as a go-to engineer for complex ML infrastructure challenges, mentor junior colleagues, and raise the engineering bar through reviews, pairing, and knowledge sharing.
  • Contribute to strategic decisions: Take ownership of technical design decisions within the team and bring informed input to long-term platform strategy discussions with product and senior engineering leadership.
  • Grow the team Play an active role in hiring, onboarding, and mentoring engineers, helping to build a strong technical culture around ML infrastructure.

WE’D LOVE TO MEET YOU IF YOU HAVE

  • Strong ML/Data platform experience 4+ years of experience building and operating ML Infrastructure or large-scale data system on cloud platform (AWS/EKS or equivalent)with a strong track record on AWS (EKS) (or equivalent cloud). Experience with mission-critical systems serving multiple teams.
  • Feature platform or data transformation expertise: Hands-on experience with data/feature engineering pipelines schema evolution, and ensuring online/offline consistency. Familiarity with feature stores (e.g., Feast, Hopsworks, SageMaker Feature Store) or a strong willingness to specialize.
  • Distributed systems builder: Skilled in container (Docker), orchestration (Kubernetes), and streaming/batch processing technologies (e.g., Kafka/Kinesis, Spark/Flink).
  • Model serving and low-latency systems: Practical experience with production serving stacks (e.g., KServe/Triton/TorchServe/custom), request shaping, caching, and traffic management.
  • Observability and reliability: Track record of building reliable systems with SLOs, monitoring, and deployment safeguards. Comfortable running incident response, capacity planning, and post-incident reviews.
  • Security and governance: Proficiency in IAM, secrets management, network boundaries and data protection. Experience embedding compliance and governance into engineering workflows..
  • Collaboration and communication: Able to work closely  with engineers, applied scientists, and product partners; able to translate requirements into reusable, reliable platform capabilities.
BONUS / NICE TO HAVE
  • Streaming and batch data for ML: Experience with feature pipelines (batch + streaming) and exposure to data contracts and schema/versioning practices to 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

Top Skills

AWS
Docker
Flink
Kafka
Kubernetes
Spark
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The Company
HQ: Berlin
10,000 Employees
Year Founded: 2008

What We Do

Welcome to Zalando. Here’s some key info about us:

Our position and vision:
- We’re Europe’s leading online platform for fashion and lifestyle
- Founded in Berlin in 2008, we bring head-to-toe fashion to more than 50 million active customers in 25 markets; offering clothes, footwear, accessories, and beauty
- Our vision is to become The Starting Point For Fashion.

Our offering:
- Our assortment of international brands ranges from world-famous names to local labels
- Our platform is a one-stop fashion destination for inspiration, innovation, and interaction
- As Europe’s most fashionable tech company, we work hard to find digital solutions for every aspect of the fashion journey: for our customers, partners, and friends of our brand.
- Our logistics network with 12 centrally located fulfillment centers allows us to efficiently serve our customers throughout Europe, supported by warehouses in Italy, France, Poland, and Sweden with a focus on local customer needs.

Our beliefs:
- Our ambition is to combine our passion for self-expression through fashion with our unwavering commitments to sustainability and D&I
- We believe that our integration of fashion, operations, and online technology gives us the capability to deliver a compelling value proposition to both our customers and fashion brand partners.

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