GenAI & Multimodal ML Scientist for EU Consumer Discovery

Amazon Inc.; Madrid; On-site; EUR 90,000 - 140,000; Full time

Job summary

Amazon Spain Services, S.L.U. is seeking an Applied Scientist for EU INTech Consumer Selection Discovery in Madrid.

You will join Tamale team to tackle complex ML problems at the intersection of e-commerce and customer experience, deploying scalable AI solutions and data products across the business. You will collaborate with business partners, scientists, and product managers to translate requirements into production-ready models, contributing to GenAI deployments and quality improvements for

Qualifications

• PhD or Master's degree in CS, CE, ML or related field.
• Experience in building models for business applications.
• Experience with patents or publications at top-tier conferences or journals.
• Experience programming in Java, C++, Python or related language.
• Experience in algorithms, data structures, parsing, numerical optimization, data mining, parallel and distributed computing, HPC.

Responsibilities

• Apply ML solutions to automate processes and improve catalog data quality.
• Translate requirements into scalable, distributed services.
• Collaborate with business partners, scientists, and product managers to deliver.
• Influence scientific roadmap and set standards of excellence.
• Work with state-of-the-art models including multimodal and GenAI.

Skills

Machine learning

Programming: Java/C++/Python

Distributed computing

Data mining/optimization

Patents or publications

Education

PhD or Master's degree in CS/CE/ML

Tools

GenAI models

Multimodal LLMs

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Job description

Amazon Spain Services, S.L.U. is seeking an Applied Scientist for EU INTech Consumer Selection Discovery in Madrid.

You will join Tamale team to tackle complex ML problems at the intersection of e-commerce and customer experience, deploying scalable AI solutions and data products across the business. You will collaborate with business partners, scientists, and product managers to translate requirements into production-ready models, contributing to GenAI deployments and quality improvements for