Senior Machine Learning Engineer, Mapp

• Strong experience building production-grade software and taking ownership of systems beyond the prototype stage
• Hands-on experience implementing and deploying machine learning solutions in production
• Strong Python and SQL skills, with experience using Spark or comparable large-scale data processing technologies
• A solid understanding of software architecture, APIs and services, testing, monitoring, and reliable production operations
• The ability to evaluate trade-offs between model quality, latency, scalability, complexity, and maintainability
• The confidence to make technical decisions independently, communicate them clearly, and collaborate closely with Data Scientists and Engineers
• Tenacity, curiosity, and the ability to quickly learn and apply new technologies and approaches
• A BSc in Computer Science or a related field, or equivalent practical knowledge and professional experience

Desirable

• Interested in helping reshape the future of retail? We are tackling some of the most challenging problems in machine learning: building personalization systems that understand fashion and turn that understanding into reliable, scalable customer experiences
• Machine learning is a core technology at Mapp Fashion. We use it to predict which clothes will appeal to customers, create personalized recommendations, reduce waste, optimize the supply chain, and turn product images and catalog data into rich, actionable metadata. Your work will directly influence shopping journeys and customer communications, both online and in-store
• This is a senior engineering role with real technical ownership. You will help define how machine learning models move from experimentation into production, make architectural decisions, and build systems that remain fast, reliable, and maintainable at scale
• Own the design, implementation, deployment, and evolution of production machine learning systems
• Design architectures that serve models efficiently under real-world constraints, including large data volumes, reliability, and latency requirements
• Build and operate ML systems across hybrid infrastructure, spanning cloud environments and our own on-premise data centers
• Partner closely with Data Scientists to turn experiments and models into robust production capabilities while enabling rapid iteration
• Make pragmatic technical decisions around model serving, data flows, observability, testing, and maintainability
• Raise engineering standards for ML systems through strong system design, high code quality, and clear technical direction
• Apply modern ML tools and approaches where they create measurable value, rather than adding complexity for its own sake
• Work independently on ambiguous, technically challenging problems while helping others make progress when needed
• The challenges you may work on:
• Building personalized product recommendations across large and constantly evolving fashion catalogs
• Developing systems that learn and adapt to customer preferences as shopping behavior changes
• Deriving rich, useful product metadata from images, catalog data, and other product signals
• Building reliable model-serving and data-processing systems for high-volume production use cases
• Balancing model quality with engineering constraints such as latency, scalability, reliability, and operational simplicity