2027 Applied Scientist Internship

Who you are

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

What the job involves

• 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
• 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
• 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 NLP and multimodal ML systems that help customers make smarter purchase decisions, developing computer vision and motion planning for autonomous robotic picking, creating LLM-based tools for automated troubleshooting and synthetic data generation, advancing multilingual ML models that scale Amazon features across languages, or tackling query optimization challenges for distributed database systems at cloud scale — 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
• We're hiring interns for multiple teams in Germany, including but not limited to
• Community Data & Science — Build ML systems that understand, structure, and present customer-generated content (ratings, text, images, and video) to help customers make better purchase decisions, spanning content moderation, ranking, summarization, and insight extraction at scale
• Vulcan Pick (Robotics) — Develop an advanced robotic picking system that autonomously identifies and extracts items from warehouse shelves — combining computer vision, robotic manipulation, ML, and motion planning to fulfill customer orders in live warehouse operations at scale
• Content Systems Science & Engineering — Develop foundational computer vision and AI for Amazon's product catalog, from vision-language models and real-time inference to AI-powered content validation, visual search, and content understanding systems
• Cosmos (INTech) — Build models, tools, and metrics that automate the multilingual expansion of ML solutions powering Amazon features across global stores and languages beyond US/English
• NDGE Science — Build LLM-based services for automated troubleshooting — including model-based and human-in-the-loop evaluation, tool-augmented reasoning, and synthetic data generation for evaluating and fine-tuning language models
• DBS Redshift — Tackle distributed database challenges at cloud scale, working on query optimization, system auto-tuning, materialized views, and query processing for Amazon Redshift, one of the most widely used cloud data warehouses
• 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