Job
- Level
- Senior
- Job Field
- Software, Data
- Employment Type
- Full Time
- Contract Type
- Permanent employment
- Location
- Berlin
- Working Model
- Hybrid, Onsite
Job Summary
In this role, you will develop scalable MLOps solutions on AWS, design low-latency data pipelines using Apache Flink and Spark, and optimize model performance and system operation in collaboration with Applied Scientists.
Job Technologies
Your role in the team
- Zalando Marketing Services (ZMS) represents a new era of marketing in fashion e-commerce. We enable fashion, beauty, and lifestyle partners to connect with over 60 million active customers across European markets on Zalando and beyond by providing access to exclusive target groups and intelligent marketing tools. We are ambitious and committed to being the starting point for fashion for our customers and partners; therefore, we are establishing and scaling our Applied Science and Engineering Backbone for ad personalization by working backwards from user, content, and context understanding.
- At ZMS Tech, we are currently advancing our real-time inference-powered prediction system and building a brand-new ad candidate retrieval system to handle millions of sponsored ad opportunities in real-time, with low latency and high volume. As a Senior ML/Data Engineer, you will work with a talented cross-functional team of applied scientists, software engineers, data engineers, product managers, and designers. Together, you will build and scale data pipelines and machine learning infrastructure for our next generation of the AdTech platform.
- Design & Architecture: Play a key role in the design, architecture, and development of end-to-end data engineering and MLOps solutions with full operational responsibility on cloud infrastructure (AWS, Databricks, Kubernetes).
- Streaming Pipelines: Gather requirements and design high-throughput, low-latency batch and real-time feature pipelines with Apache Flink (Java) and Spark (Python) to deploy production-ready features in our central Hopsworks Feature Store.
- System Operationalization: Drive the operationalization, model serving, and maintenance (MLOps/MLaaS) of our real-time inference-powered prediction system and new ad candidate retrieval systems.
- Scientific Collaboration: Work closely with Applied Scientists to optimize the runtime of data pipelines, data quality, and model performance, latency, and memory usage.
- Operational Excellence: Take responsibility for the operational excellence of our AI systems by implementing robust CI/CD pipelines, continuous monitoring, and automated alerting for distributed systems to maximize scalability and reliability.
- Communication & Roadmapping: Communicate effectively with product managers, data scientists, and technical colleagues by translating complex technical concepts into actionable roadmaps.
- Workflow Automation: Strive to continuously improve and automate the time-to-market for your team's experiment-to-production workflows.
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Our expectations of you
Education
- Solid foundation: You have a degree in computer science, a related technical field, or possess equivalent practical experience demonstrating strong software engineering fundamentals.
Qualifications
- Strong programming skills: You are proficient in Java and/or Python and have a strong passion for writing clean, testable, and maintainable production code. Familiarity with ML libraries (e.g., PyTorch, TensorFlow) is a significant plus.
- Modern practices: You are well-versed in agile methodologies, CI/CD pipelines, and the establishment of effective metrics and monitoring for large-scale distributed systems.
Experience
- Streaming & Data Engineering: You have extensive hands-on experience in designing, building, and maintaining high-throughput, low-latency data streaming applications. Practical experience with Apache Flink and Spark is highly required.
- MLOps & Model Serving: You have professional experience in operationalizing machine learning, model serving (e.g., Triton, SageMaker), data version control, and workflow orchestration (e.g., Airflow or Databricks workflows).
- Collaboration & Mentoring: You have experience working closely with applied scientists and mentoring other engineers, with excellent verbal and written communication skills to bridge technical gaps between stakeholders.
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What we offer
- 27 days of holiday per year to start for full-time employees (+1 day for each calendar year up to 30 days).
- 2 paid volunteering days a year.
- Hybrid working model with up to 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.
- Employee shares program.
- 40% Rabatt auf Mode- und Schönheitsprodukte, die von Zalando verkauft und versendet werden, 30% Rabatt auf Lounge by Zalando, Rabatte von externen Partnern.
- 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.
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Benefits
Work-Life-Integration
Food & Drink
Health, Fitness & Fun
Topics that you deal with on the job
Job Locations
This is your employer
Zalando SE
Zalando is the leading online fashion platform in Europe, connecting customers, brands, and partners in 17 markets. We offer the latest styles and trends from the best fashion brands around the world, all in one place. With Zalando, you can find the perfect outfit for any occasion, whether you're looking for casual wear, formal wear, or something in between. We also offer a wide range of sizes and styles to suit every customer's needs. Shop with us today and see why we're the leading fashion destination in Europe.
Description
- Company Type
- Established Company
- Working Model
- Full Remote, Hybrid, Onsite
- Industry
- Trade
Dev Reviews
by devworkplaces.com
Total
(1 Review)3.4
Workingconditions
4.4Culture
3.5Engineering
3.1Career Growth
2.6
Senior Machine Learning Engineer
- Location
- Berlin
- Working Model
- Hybrid, Onsite
- Diversity
- Open for all genders
