2026 Applied Scientist Intern

Job Description

Job Title: 2026 Applied Scientist Intern, Amazon. Location: Multiple EMEA locations: Austria, Estonia, France, Germany, Ireland, Israel, Italy, Jordan, Luxembourg, Netherlands, Poland, Romania, Spain, South Africa, UAE, UK

Job Type: Internship, full-time for 3–6 months. Interns will design, implement, and deploy end-to-end systems, contributing to solutions for complex, never-before-solved problems across Amazon Science.

Duties:

• Design and develop technical solutions and end-to-end systems.
• Collaborate with Amazon scientists and other interns on research and production-level projects.
• Create technical white papers, roadmaps, and presentations;
• Develop new algorithms, models, or solutions tailored to real-world challenges.
• Apply statistical analysis and experimental design to evaluate methods.
• Participate in team events, networking, and opportunities to publish in top-tier conferences;
• Work with diverse cross-functional teams to solve complex business and technical problems.

Qualifications:

• Enrolled in a Master’s or PhD program in Computer Science, Machine Learning, Engineering, Operations Research, Statistics, or a related field.
• Experience programming in Python, Java, or C++;
• Ability to design experiments and analyze results statistically.
• Familiarity with implementing algorithms using toolkits or self-developed code;
• Experience in machine learning, data mining, or statistical algorithms is preferred.
• Publications in peer-reviewed conferences or journals are a plus.

Benefits and Instructions:

• Competitive stipend/salary for the duration of the internship.
• Mentorship and pairing with an experienced manager.
• Access to professional networking, intern programs, and site events;
• Opportunity to contribute to impactful Amazon Science projects and publish research.
• Start dates available throughout the year; internship duration varies from 3–6 months.
• Internships are not remote; candidates must work at assigned EMEA locations;
• Applications are reviewed on a rolling basis; candidates are assigned to teams based on research interests and experience.