2027 Research Science Internship - United States, PhD Student Science Recruiting

AI in this role

Conduct research and develop scalable machine learning models as a Research Science Intern.

pythonmachine-learning

statistical-modelingalgorithm-designdata-analysisresearch

Do you enjoy solving challenging problems and driving innovation in research? Do you want to develop scalable models and apply machine learning techniques to guide real-world decisions? We are looking for builders, innovators, and entrepreneurs who want to bring their ideas to reality and improve the lives of millions of customers.
As a Research Science Intern, you will apply advanced statistical techniques and emerging AI/ML technologies to solve complex problems, implement prototypes, and work with massive datasets. You'll find yourself at the forefront of innovation, shaping the future of Amazon's products, services, and operations.
Imagine waking up each morning, fueled by the excitement of solving intricate problems that have a direct impact on Amazon's excellence. Your day might begin by collaborating with cross-functional teams, exchanging ideas and insights to develop innovative solutions. You'll immerse yourself in a world of data, leveraging your expertise in areas such as optimization, machine learning, statistical modeling, and algorithmic research to uncover hidden patterns and drive meaningful impact.
Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research, and mentorship from industry leaders. This immersive experience will sharpen your technical skills and cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment where bold ideas are celebrated.
Amazon has positions available for Research Science Internships in, but not limited to, Bellevue, WA; Boston, MA; Cambridge, MA; New York, NY; Santa Clara, CA; Seattle, WA; Sunnyvale, CA, Arlington, VA

Key job responsibilities
• Conduct research activities including data collection, analysis, and interpretation under the guidance of senior researchers
• Develop and test hypotheses using appropriate scientific methodologies and computational tools
• Document findings, maintain detailed research records, and prepare reports summarizing results and insights
• Collaborate with team members to troubleshoot challenges and refine experimental approaches
• Participate in team meetings and present progress updates on assigned research projects

A day in the life
As a Research Science Intern, you'll immerse yourself in hands-on scientific work, collaborating with our research team on projects that span data analysis, experimental design, and computational modeling. Your day might include conducting literature reviews, running simulations, analyzing datasets, and participating in team discussions where your insights contribute to ongoing research initiatives. You'll have opportunities to present findings, learn from mentors, and develop practical skills in a supportive research environment that values curiosity and collaborative problem-solving.

Basic qualifications

- Are enrolled in a PhD in computer science, machine learning, engineering, or related fields
- Can relocate to where the internship is based
- Must be available for full-time (40 hours per week) internship for the whole duration of the internship
- Demonstrated science depth in a specific research area, evidenced through publications, thesis work, or equivalent contributions

Preferred qualifications

- • Experience with statistical analysis software or data visualization tools
- • Prior research experience through academic projects, laboratory work, or internships
- • Knowledge of machine learning or advanced computational methods

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

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.

The starting pay for this position is listed below. Starting Day 1 of employment, Amazon offers EAP, Mental Health Support, Medical Advice Line, 401(k) matching. Learn more about our benefits at https://hiring.amazon.com/why-amazon/benefits.

Seattle, WA, USA - 129,200.00 USD Annually

How we rate this

2027 Research Science Internship - United States, PhD Student Science Recruiting at Amazon rates 90 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. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Statistical ModelingAlgorithm DesignData AnalysisResearchPythonMachine Learning

Questions you could be asked

Tell me about a project where statistical modeling was part of your work. What did you do?
Tell me about a project where algorithm design was part of your work. What did you do?
Tell me about a project where data analysis was part of your work. What did you do?
Tell me about a project where research was part of your work. What did you do?
Walk me through how you've used Python in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Statistical Modeling, Algorithm Design, Data Analysis, Research, and Python. 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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