2027 Applied Scientist Internship – PhD, Amazon University Talent Acquisition

AI in this role

PhD Applied Science Intern to design, research, and deploy end-to-end machine learning and AI solutions into production across various Amazon domains.

python

computer-visionapplied-sciencemachine-learningalgorithm-designresearchdeep-learning

We're building systems that turn research into real-world impact — ensuring flawless streaming for millions of Prime Video customers, shaping the future of AI-driven shopping with Rufus, advancing speech generation and conversational AI, optimizing large-scale infrastructure through intelligent observability, and transforming how people and jobs find each other. If this sounds interesting to you then we have a range of opportunities for you to explore.

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 building computer vision models that detect quality issues across Prime Video's content library, developing multimodal AI solutions that power Amazon's shopping assistant Rufus, creating automated reasoning tools that verify distributed systems at scale, engineering intelligent observability frameworks for large-scale infrastructure, or advancing recommendation models that connect people with the right jobs — 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 the UK, including but not limited to;

• Prime Video Video Quality Analysis – Develops AI and machine learning solutions using computer vision, audio processing, and generative AI to detect and prevent streaming quality issues across Prime Video's vast content library
• Rufus Features Science UK – Shapes AI-driven shopping experiences at Amazon, working on projects from enabling Rufus to take actions on behalf of customers to generating multimodal answers combining text, image, audio, and video.
• READI – Observability, Triage & Peak Readiness – Builds intelligent log analytics and automated performance frameworks that transform system telemetry into actionable insights for large-scale distributed systems.
• Automated Reasoning Group – Ensures program and systems correctness through deductive proof, model checking, formal verification, and runtime conformance monitoring for large-scale distributed systems.

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.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

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 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. Open the description and count.

Key facts

2027 Applied Scientist Internship – PhD, Amazon University Talent Acquisition is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in GB, London.
The job was posted on October 2, 2026.
Seniority: intern.
Applications are handled by Amazon. Apply on Amazon

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 VisionApplied ScienceMachine LearningAlgorithm DesignResearchDeep LearningPython

Questions you could be asked

Walk me through a computer vision problem you solved, from raw data to a deployed model.
Tell me about a project where applied science was part of your work. What did you do?
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where algorithm design was part of your work. What did you do?
Tell me about a project where research was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Computer Vision, Applied Science, Machine Learning, Algorithm Design, and Research. 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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