2027 Applied Scientist Internship – PhD, Amazon University Talent Acquisition

AI in this role

Design and deploy end-to-end machine learning, computer vision, and autonomous agentic systems as a PhD Applied Science Intern.

python

ai-agentscomputer-visionmachine-learningmultimodal-aiagentic-systemsalgorithm-design

We're building the intelligence behind how customers discover, trust and enjoy products on Amazon. We're solving complex catalogue quality challenges with machine learning, enhancing product discovery through computer vision and multimodal AI and pioneering agentic systems that autonomously navigate and stress-test the Amazon shopping experience to surface insights at scale.

We're looking for PhD students across multiple research domains to invent, design, and implement state of the art solutions for never before solved problems. Your work here won't just stay in a notebook, it ships to production and reaches customers worldwide.

Check out the details below including the job responsibilities, team details, and basic qualifications before submitting your application.

You can find more information about the Amazon Science community as well as interview preparation tips via the links below;

- https://www.amazon.science/
- https://amazon.jobs/content/en/career-programs/university/science
- https://amazon.jobs/content/en/how-we-hire/university-roles/applied-science

Key job responsibilities
As an Applied Science Intern, you will own the design and development of end-to-end systems. You'll have the opportunity to write technical white papers, create roadmaps and drive production level projects that will support Amazon Science.

You will work closely with Amazon scientists and other science interns to develop solutions and deploy them into production. You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon's customer focused culture.

The ideal intern should have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems.

A day in the life
You'll spend your first weeks scoping your project with your mentor, then own the research and implementation end-to-end.

Your work could involve developing machine learning and data analysis solutions that detect and resolve catalogue quality issues at massive scale, building computer vision and multimodal learning models that transform how customers discover and interact with products, engineering universal ML systems that make shopping on Amazon easier and more visually delightful, or creating autonomous agentic shoppers that tirelessly navigate the Amazon website to provide feedback and actionable insights — depending on the team you're matched with.

Many interns publish at top-tier conferences or see their work deployed to production before the internship ends. Interns may also be considered for a return offer at the end of their internship, subject to performance evaluation and headcount availability.

Further benefits of an Amazon Science internship include;

- All of our internships offer a competitive salary
- Interns are paired with an experienced manager and mentor(s)
- Interns get invited to different intern program or office events
- Interns can build their professional and personal network with other Amazon Scientists
- Interns can potentially publish work at top tier conferences

About the team
We're hiring interns for multiple teams in Spain including but not limited to;

•Tamale - Using Machine Learning and Data analysis solutions to solve complex catalogue quality problems
• NintAI- Developing AI solutions, focusing on computer vision and multimodal learning to enhance how customers discover and interact with products
• Home Innovation tech- Building universal, state of the art Machine Learning technology that makes shopping on Amazon easier and more visually delightful for our customers
• EU Intech- Pioneers a population of agentic shoppers, autonomous AI agents, that tirelessly navigate and shop on the Amazon website, providing feedback and insights to improve the customer experience

You'll submit a single application and we'll match you with science teams best aligned with your research interests.

Applications are reviewed on a rolling basis, and your application stays active until we find a team match or confirm there are no matches available.

Start dates are available throughout the year for durations of between 3–6 months. Please note, each team has different start date and duration preferences — your recruiter will confirm the preferences of the team you're matched with prior to interviewing.

We offer science internships in multiple locations across the EMEA region and you can indicate your interest in all these locations by applying here (Austria, Estonia, France, Germany, Ireland, Israel, Italy, Jordan, Luxembourg, Netherlands, Poland, Romania, South Africa, Spain, Sweden, UAE, and UK).

Please note we do not offer remote internships.

Basic qualifications

- Are enrolled in a PhD in computer science, machine learning, engineering, or related fields
- Experience programming in Java, C++, Python or related language
- Speak, write, and read fluently in English

Preferred qualifications

- Have publications at top-tier peer-reviewed conferences or journals
- Experience in solving business problems through machine learning, data mining and statistical algorithms
- Experience in designing experiments and statistical analysis of results
- Experience implementing algorithms using toolkits and self-developed code

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

2027 Applied Scientist Internship – PhD, Amazon University Talent Acquisition at Amazon rates 100 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. Open the description and count.

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

AI AgentsComputer VisionMachine LearningMultimodal AIAgentic SystemsAlgorithm DesignPython

Questions you could be asked

How do you decide when an AI agent can act on its own versus asking for approval first?
Walk me through a computer vision problem you solved, from raw data to a deployed model.
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where multimodal ai was part of your work. What did you do?
Tell me about a project where agentic systems was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: AI Agents, Computer Vision, Machine Learning, Multimodal AI, and Agentic Systems. 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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