Applied Scientist, EU INTech Consumer Selection Discovery, NintAI

Amazon is hiring an Applied Scientist, EU INTech Consumer Selection Discovery, NintAI in Madrid, Spain. Level rates it ; you can apply on Amazon.

Level's read on this job

Level rates this applied scientist role Builds AI ●●●●: the job is building AI systems.
The posting mentions Python, Machine Learning Models and computer vision among its tools and skills.
Level classes it as a mid-level role.
Amazon has 20,000+ other open roles on Level.

AI in this role

Design, develop and deploy end-to-end AI/ML science solutions for global search and discovery in consumer experiences.

PythonMachine Learning Models

computer-visionnlpgenerative-airecommendation-systemsranking

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.

How we rate this

Applied Scientist, EU INTech Consumer Selection Discovery, NintAI at Amazon rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Applied Scientist, EU INTech Consumer Selection Discovery, NintAI is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in ES, M, Madrid.
The job was posted on June 16, 2026.
Seniority: mid.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Computer visionNLPGenerative AIRecommendation SystemsRankingPythonMachine Learning Models

Also common in applied scientist jobs at this level

LLM Reinforcement learning TensorFlow Spark AWS scikit-learn

Pay

Typical applied scientist job: $167k

This posting doesn't list pay. Use the typical applied scientist figure when pay comes up. See applied scientist jobs

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?
Tell me about a project where generative ai was part of your work. What did you do?
Tell me about a project where recommendation systems was part of your work. What did you do?
Tell me about a project where ranking was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Computer vision, NLP, Generative AI, Recommendation Systems, and Ranking. 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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