Machine Learning Engineer

This opportunity is part of a newly launched ML initiative within one of Europe’s leading fashion and retail companies, operating at large scale across multiple markets and serving millions of customers.

The team is building a shared Machine Learning platform that will enable multiple product teams to develop, deploy, and scale ML solutions reliably in production.

This is a platform-level role focused on evaluating, designing, and rolling out reusable ML capabilities across the organization — from proof of concept to early production adoption. The position combines hands-on engineering with architectural input, helping define how ML infrastructure will operate at scale across the company.

Responsibilities

• Support and contribute hands-on to multiple ML platform POCs
• Work closely with Applied Scientists, ML Engineers, and internal Platform teams
• Evaluate platform capabilities (GPU training, real-time and batch inference, orchestration, scalability)
• Analyze performance, reliability, and integration with existing systems
• Contribute to key technical decisions (build vs buy, tooling, target stack)
• Support early production rollout and integration into the broader ecosystem
• Help define best practices, governance standards, and onboarding guidelines
• Enable adoption of the platform by initial and scaling teams

Requirements

• 5+ years building and operating production-grade ML or large-scale data systems on cloud platforms
• Experience with distributed systems (Docker and Kubernetes)
• Experience with streaming or batch processing systems (e.g. Kafka, Spark, Flink)
• Experience with ML lifecycle orchestration and workflow systems (e.g. Metaflow / Outerbounds, Airflow, Kubeflow Pipelines, Dagster, Flyte, Prefect)
• Experience designing reproducible ML pipelines and supporting model training, evaluation, and deployment workflows across teams
• Strong understanding of reliability, monitoring, and safe deployment practices
• Experience integrating multiple tools into a coherent, production-ready ML platform
• Ability to contribute to architectural decisions and communicate technical trade-offs clearly

• Experience running ML workloads on Kubernetes (GPU or multi-tenant environments)
• Experience with enterprise ML platforms (e.g. Databricks or similar)
• Experience onboarding teams onto shared platforms and optimizing infrastructure costs

Perks:

• Paid Vacation
• Sick Days
• Floating Holidays
• Sport/Insurance Compensation
• English Classes
• Charity

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