Amazon Applied Scientist 2028 – Job Responsibilities, Qualifications Required
Are you excited about the idea of seeing your research make a real difference in how millions of customers discover, explore, and purchase products on Amazon?
Amazon’s Japan Store Tech team works on the science and technology that power cross-border shopping across Japan and markets around the world. The team focuses on areas such as product discovery, search relevance, personalization, and content experiences across multiple marketplaces.
The challenges are exciting and highly technical. You’ll work with multilingual signals, data from different marketplaces, and region-specific customer behaviors while building solutions that need to operate quickly and reliably for millions of shoppers every day.
Amazon is looking for talented Bachelor’s and Master’s students who are passionate about applied science and machine learning to join the team as Applied Scientists in 2028.
Amazon Applied Scientist 2028: Research Areas
Students with experience or specialization in one or more of the following areas are encouraged to apply:
- Machine Learning
- Deep Learning
- Natural Language Processing (NLP)
- Information Retrieval
- Recommender Systems
- Computer Vision
- Large Language Models (LLMs)
- Generative AI
- Causal Inference
- Experimentation and A/B Testing
- Optimization
- Other related areas of applied science and machine learning
As an Applied Scientist, you’ll have the opportunity to work on real-world problems rather than research in isolation. You’ll develop new models and algorithms, design and run experiments using live traffic, and take ownership of meaningful scientific projects from the initial idea through implementation.
You’ll also get the opportunity to work with Generative AI and LLM-based systems, helping improve both customer-facing experiences and internal tools used by Amazon teams.
If you’re looking to begin your research career by solving challenging problems at global scale, this role offers the chance to work alongside experienced scientists and engineers in a collaborative and international environment.
Application Timeline
- Application & Assessment Deadline: September 27, 2026
- Selection Communication Begins: October 5, 2026
- Offer Extensions: December 2026 – March 2027
Amazon Applied Scientist 2028: Key Job Responsibilities
As an Applied Scientist, you’ll work closely with scientists, software engineers, and other technical teams to develop solutions that improve the customer experience.
Your responsibilities may include:
- Working with experienced Amazonians from different technical backgrounds to design, develop, and deploy innovative machine learning models and scientific solutions.
- Building scalable machine learning models and algorithms for areas such as product discovery, search relevance, personalization, and other customer-facing experiences.
- Designing and conducting both offline and online experiments to test ideas, validate hypotheses, and measure the real-world impact of your solutions.
- Working with large-scale datasets to uncover useful patterns, generate insights, and guide model and algorithm development.
- Exploring and contributing to Generative AI and LLM-powered solutions that can improve customer experiences as well as development productivity.
- Keeping up with new developments in machine learning, AI, and related technologies and finding ways to apply them to real-world problems.
- Writing clean, reliable, and maintainable production-quality code.
- Sharing your research and technical findings through documentation, presentations, and technical papers.
- Working in an agile environment and partnering closely with software engineers to move research ideas and prototypes into production.
Amazon Applied Scientist 2028: Basic Qualifications
To be considered for this opportunity, you should:
- Be currently enrolled in a Bachelor’s or Master’s degree program in Computer Science, Machine Learning, Statistics, Applied Mathematics, Electrical Engineering, or another related quantitative field.
- Have programming experience with at least one language such as Python, Java, C++, or an equivalent language.
- Have coursework or research experience in areas such as machine learning, deep learning, NLP, information retrieval, computer vision, statistics, or optimization.
- Have a solid understanding of data structures, algorithms, and mathematical or statistical modeling.
- Be expected to graduate in 2028.
- Be available to start on either April 1, 2028, or October 1, 2028.
Amazon Applied Scientist 2028: Preferred Qualifications
While not required, the following experience can strengthen your application:
- Research publications or submitted papers at leading conferences such as NeurIPS, ICML, ACL, EMNLP, CVPR, KDD, WWW, RecSys, or SIGIR.
- Practical experience developing and deploying machine learning models using large-scale datasets.
- Experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX.
- Familiarity with Large Language Models, Generative AI, or Retrieval-Augmented Generation (RAG).
- Experience designing, running, or analyzing A/B tests or causal inference experiments.
- Strong written and verbal communication skills, with the ability to explain complex technical concepts clearly.
- Experience working with cloud computing platforms such as AWS.
- Japanese language proficiency is a plus, but it is not required.
- English fluency is required.
Why Consider This Opportunity?
This role is a great opportunity for students who want to take their machine learning and research skills beyond the classroom and apply them to problems that affect millions of customers.
You’ll have the opportunity to work on large-scale systems, explore emerging AI technologies, collaborate with experienced scientists and engineers, and see your ideas move from research and experimentation into real products and services.
For students graduating in 2028 and interested in machine learning, AI, research, and applied science, Amazon’s Japan Store Tech team offers an exciting environment to start building a career in science and technology.