2027 Applied Scientist Internship – PhD, Amazon University Talent Acquisition

2027 Applied Scientist Internship – PhD, Amazon University Talent Acquisition
Job ID: | Amazon Development Centre Italy SRL
We're building systems that turn research into real world impact. We're advancing the intelligence behind how customers converse, connect and interact with Alexa across the globe and we're tackling complex conversational AI challenges with state of the art GenAI systems.
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;
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.
Projects may include developing and implementing real-time AI services, conducting comprehensive analysis of GenAI algorithms and methodologies or contributing to the creation of reliable, efficient code for production systems that serve millions of users—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
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
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
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