2027 Applied Scientist Internship – PhD, Amazon University Talent Acquisition

Amazon is hiring a 2027 Applied Scientist Internship – PhD, Amazon University Talent Acquisition in Berlin, Germany. Level rates it ; you can apply on Amazon.

Level's read on this job

On Level's scale, this applied scientist role is Builds AI ●●●●: the job is building AI systems.
Tools and skills the posting cites include Python, fine tuning and computer vision.
Level classifies it as an internship.
Level lists 21,000+ other open roles at Amazon.

AI in this role

Conduct PhD-level research and development of state-of-the-art machine learning, NLP, and computer vision systems for production at Amazon.

python

fine-tuningcomputer-visionnlpmachine-learningdeep-learningllms

We're building systems that turn research into real-world impact — helping hundreds of millions of customers make better purchase decisions through AI-powered content understanding, enabling robots to autonomously fulfill customer orders at warehouse scale, advancing multilingual ML to expand Amazon features across global markets, building LLM-based services that automate complex troubleshooting, and optimizing query processing for one of the world's most widely used cloud data warehouses. 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 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.

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 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.

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 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

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.

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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.

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How we rate this

2027 Applied Scientist Internship – PhD, Amazon University Talent Acquisition at Amazon rates 95 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. Read the description and count the AI tools, models and workflows it names.

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 DE, BE, Berlin.
The job was posted on October 9, 2026.
Seniority: intern.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Fine-tuningComputer visionNLPMachine learningDeep learningLLMsPython

Also common in applied scientist jobs at this level

Deep learning Generative AI TensorFlow Spark Reinforcement learning AWS

Pay

Typical applied scientist job: $169k

This posting doesn't list pay. Use the typical applied scientist figure when pay comes up. See applied scientist jobs

Questions you could be asked

Walk me through fine-tuning a model: what data did you use, and how did you check the result?
Walk me through a computer vision problem you solved, from raw data to a deployed model.
What NLP problem have you worked on, and how did you measure whether it actually worked?
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where deep learning was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Fine-tuning, Computer vision, NLP, Machine learning, and Deep learning. 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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