Job
- Level
- Senior
- Job Feld
- Data, Back End
- Anstellung
- Vollzeit
- Vertragsart
- Unbefristetes Dienstverhältnis
- Ort
- Berlin
- Arbeitsmodell
- Onsite
Job Zusammenfassung
In dieser Rolle entwickelst du skalierbare ML-Infrastruktur und optimierst Online-Serving-Services mithilfe von Kubernetes, während du auch die Automatisierung und Sicherheit von Plattformarbeiten vorantreibst.
Job Technologien
Deine Rolle im Team
- Our ML Platform team builds the core ML platform capabilities powering Zalando's AI-native experiences. We provide low-latency features, embeddings, real-time inference infrastructure, and scalable ML platform capabilities 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 evolving the next generation of Kubernetes-native AI runtime infrastructure, enabling scalable online serving, distributed GPU workloads, and self-service ML platform operations across the company.
- 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, Kubernetes-native serving infrastructure, and platform automation, while also mentoring peers and contributing to engineering best practices across the team.
- Own the design and implementation of scalable real-time feature platforms, online serving infrastructure, and distributed ML runtime systems. Bring strong technical judgment to ensure our platform foundations are reliable, reusable, and operationally mature.
- Deliver and maintain SLOs for feature freshness, data quality, online/offline consistency, and runtime reliability; implement monitoring, observability, and safe deployment practices.
- Drive automation and self-service (IaC, GitOps, CI/CD), reusable deployment templates, and operational tooling that reduce friction and accelerate time-to-first-success for applied scientists and engineers.
- Contribute to reusable platform integrations and deployment automation that improve how ML systems interact with developer tooling and internal AI platform capabilities.
- Implement identity and access management, secrets management, network isolation, and data governance built in from the start to ensure compliance and trustworthiness by default.
- Act as a key technical contributor for complex ML infrastructure challenges, mentor junior colleagues, and raise the engineering bar through reviews, pairing, and knowledge sharing.
- Take ownership of technical design decisions within the team and bring informed input to long-term platform and runtime infrastructure strategy decisions with product and senior engineering leadership.
- Play an active role in hiring, onboarding, and mentoring engineers, helping to build a strong technical culture around ML infrastructure and platform engineering.
Unsere Erwartungen an dich
Qualifikationen
- You have a background in building or integrating developer tooling, platform automation, or workflow systems, including emerging AI-assisted development or agentic workflows.
- You have a track record of building reliable systems with SLOs, monitoring, and deployment safeguards, and are comfortable handling incident response and capacity planning.
- You have strong collaboration and communication skills, enabling you to work effectively with engineers, applied scientists, and product partners to translate requirements into reliable platform capabilities.
Erfahrung
- You have 5+ years of experience building and operating ML Infrastructure or large-scale distributed systems on a cloud platform (AWS/EKS or equivalent), with strong skills in containerization (Docker), Kubernetes, and streaming/batch processing (e.g., Kafka/Kinesis, Spark/Flink).
- You have hands-on experience with data/feature engineering pipelines, schema evolution, and ensuring online/offline consistency, with familiarity with feature stores (e.g., Feast, SageMaker).
- You are experienced in designing and operating low-latency, high-scale distributed systems that meet strict throughput targets, including caching, request shaping, and traffic management.
- You have experience operationalizing Kubernetes-native ML workloads (e.g., model serving, deployment, runtime) using technologies like NVIDIA Triton, MLflow, Kubeflow, or ZenML.
- You are proficient in security and governance (e.g., IAM, secrets management, network boundaries) and have experience embedding compliance into engineering workflows.
Unser Angebot
- 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.
- 27 days of holiday a year to start for full-time employees (+1 day for every calendar year up to 30 days).
- 2 paid volunteering days a year.
- Employee shares program.
- 40% off fashion and beauty products sold and shipped by Zalando, 30% off Lounge by Zalando, discounts from external partners.
- 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.
Benefits
Work-Life-Integration
Essen & Trinken
Gesundheit, Fitness & Fun
Themen mit denen du dich im Job beschäftigst
Job Standorte
Das ist dein Arbeitgeber
Zalando SE
Zalando is die europäische Online-Plattform für Mode, die Kunden, Marken und Partner in 17 Märkten verbindet.
Description
- Unternehmenstyp
- Etablierte Firma
- Arbeitsmodell
- Full Remote, Hybrid, Onsite
- Branche
- Handel
Arbeitgeber-Reviews
von devworkplaces.com
Gesamt
(1 Bewertung)3.4
Culture
3.5Workingconditions
4.4Engineering
3.1Career Growth
2.6
Senior Software Engineer
- Ort
- Berlin
- Arbeitsmodell
- Onsite
- Diversität
- Für alle Personen geeignet (m/w/d)
- Nur Englisch
- Nur Englisch erforderlich
