AmazonPosted 3mo ago

Applied Scientist, EU INTech Consumer Selection Discovery, NintAI

Applied Scientist, EU INTech Consumer Selection Discovery, NintAI at Amazon scores 98 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

ES, M, Madridfull-time

AI in this role

computer-visionnlp

Amazon's EU International Technology (EU INTech) organisation is creating new ways for customers to discover products through innovative customer experiences. We are a science-only team within EU INTech, responsible for designing and developing AI/ML science solutions that support business needs across Amazon's global search and discovery experiences. Our mission is to make Amazon navigation easier for customers worldwide. We achieve this through two strategic pillars: making Amazon navigation more visual and improving Amazon navigation with more inspiring discovery tools and narrowing navigation.

To support this vision, we build and deploy AI/ML models that surface the most relevant content to hundreds of millions of Amazon customers worldwide. Our team comprises Applied Scientists and we partner with other teams, collaborating with ML Engineers, Software Developers, Product Managers, Technical Product Managers, and UX Designers. We are located in the Madrid Technical Hub.

We are looking for Applied Scientists who are passionate about solving highly ambiguous and challenging problems at global scale. This is a hands-on, end-to-end applied science role where you will own the full lifecycle of science solutions — from business problem analysis and science plan design, through development and experimentation, to production deployment. We are looking for AI/ML experts with knowledge on ranking, computer vision, recommendation systems, search, and customer experience design.

What makes this role unique:
• End-to-end ownership – You will analyse business problems, map them to science plans, and design and develop solutions from ideation to production. We are owners of the full science lifecycle.
• Applied science with a research edge – While our focus is on delivering applied science solutions that drive measurable business impact, our team actively pushes the state of the art in areas such as computer vision and Generative AI.
• Hands-on execution – We need scientists who thrive in building, experimenting, and shipping.

What are we looking for?
• A scientist who can independently analyse any business problem and design a rigorous science approach to solve it
• Strong hands-on engineering skills — you build and ship, not just theorise
• Deep expertise in one or more of: computer vision, generative AI, recommendation systems, ranking, or NLP
• Experience taking ML models from research to production at scale
• Comfort with ambiguity and the ability to structure complex, undefined problems
• A passion for customer-centric innovation and measurable impact
• A strong communicator capable to adapt the message from a science audience, to engineering or leadership

Key job responsibilities
• Analyse complex business problems and translate them into well-defined science plans with clear milestones and success criteria
• Design, develop, and deliver ML/AI models end-to-end — from research and prototyping through to production systems at Amazon scale and extending solutions going beyond the state of the art
• Work with state-of-the-art models in computer vision, ranking and generative AI to power new customer experiences globally
• Own major science challenges for the team, driving solutions from ideation through experimentation to production deployment
• Collaborate with a variety of roles and partner teams around the world to deliver integrated solutions
• Influence scientific direction and best practices across the team
• Maintain high quality standards on team deliverables
• Contribute to expanding the state of the art in computer vision, ranking and GenAI through publications and internal knowledge sharing

Basic qualifications

- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience building machine learning models or developing algorithms for business application
- PhD and experience in Computer Vision, Generative AI, Ranking, Deep Learning, Recommending
- Systems, Natural Language Processing or related field

Preferred qualifications

- Experience using Unix/Linux
- Experience in professional software development
- Experience developing and implementing deep learning algorithms, particularly with respect to computer vision algorithms

Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Computer VisionNlp

Questions you could be asked

Walk me through a computer vision problem you solved, from raw data to a deployed model.
What NLP problem have you worked on, and how did you measure whether it actually worked?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: Computer Vision and Nlp. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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