2026 Applied Scientist Intern, Amazon University Talent Acquisition

Amazon

full-time

Posted: October 23, 2025

Number of Vacancies: 1

Job Description

Are you a MS or PhD student interested in a 2026 internship in the field of machine learning, deep learning, generative AI, large language models and speech technology, robotics, computer vision, optimization, operations research, quantum computing, automated reasoning, or formal methods? If so, we want to hear from you!We are looking for students interested in using a variety of domain expertise to invent, design and implement state-of-the-art solutions for never-before-solved problems.You can find more information about the Amazon Science community as well as our interview process via the links below;https://www.amazon.science/https://amazon.jobs/content/en/career-programs/university/sciencehttps://amazon.jobs/content/en/how-we-hire/university-roles/applied-scienceKey job responsibilitiesAs 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 must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A day in the lifeAt Amazon, you will grow into the high impact person you know you’re ready to be. Every day will be filled with developing new skills and achieving personal growth.How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow.Some more benefits of an Amazon Science internship include;• All of our internships offer a competitive stipend/salary• Interns are paired with an experienced manager and mentor(s)• Interns receive invitations to different events such as intern program initiatives or site events• Interns can build their professional and personal network with other Amazon Scientists• Interns can potentially publish work at top tier conferences each yearAbout the teamApplicants will be reviewed on a rolling basis and are assigned to teams aligned with their research interests and experience prior to interviews.Start dates are available throughout the year and durations can vary in length from 3-6 months for full time internships.This role may available across multiple locations in the EMEA region (Austria, Estonia, France, Germany, Ireland, Israel, Italy, Jordan, Luxembourg, Netherlands, Poland, Romania, Spain, South Africa, UAE, and UK).Please note these are not remote internships.

Locations

  • United Kingdom, London, London, England, United Kingdom
  • United Kingdom, Cambridge, Cambridge, England, United Kingdom
  • United Kingdom, Edinburgh, Edinburgh, Scotland, United Kingdom
  • Israel, Haifa, Haifa, HA, Israel
  • Romania, Iasi, Iasi, Romania
  • Estonia, Tallinn, Tallinn, Estonia

Salary

Salary not disclosed

Estimated Salary Rangemedium confidence

65,000 - 85,000 USD / yearly

Source: ai estimated

* This is an estimated range based on market data and may vary based on experience and qualifications.

Skills Required

  • - Experience programming in Java, C++, Python or related languageintermediate

Required Qualifications

  • - Experience programming in Java, C++, Python or related language (experience)
  • - Enrolled in a PhD or Master's degree in Computer Science, Machine Learning, Engineering, Operations Research, Statistics or related fields (degree in a phd or master)

Preferred Qualifications

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

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

Benefits

  • general: of an Amazon Science internship include;
  • general: • All of our internships offer a competitive stipend/salary
  • general: • Interns are paired with an experienced manager and mentor(s)
  • general: • Interns receive invitations to different events such as intern program initiatives or site events
  • general: • Interns can build their professional and personal network with other Amazon Scientists
  • general: • Interns can potentially publish work at top tier conferences each year
  • general: About the team
  • general: Applicants will be reviewed on a rolling basis and are assigned to teams aligned with their research interests and experience prior to interviews.
  • general: Start dates are available throughout the year and durations can vary in length from 3-6 months for full time internships.
  • general: This role may available across multiple locations in the EMEA region (Austria, Estonia, France, Germany, Ireland, Israel, Italy, Jordan, Luxembourg, Netherlands, Poland, Romania, Spain, South Africa, UAE, and UK).
  • general: Please note these are not remote internships.

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Application Instructions

https://www.amazon.jobs/en/jobs/3115180

Tags & Categories

auta-scienceamazon.artificial-intelligenceMachine Learning Science