AmazonPosted 1mo ago

【Class of 2028／Full-Time】Applied Scientists , Amazon International Stores, Amazon Japan Store Tech

【Class of 2028／Full-Time】Applied Scientists , Amazon International Stores, Amazon Japan Store Tech at Amazon scores 100 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

JP, 13, Tokyofull-time

AI in this role

pytorchtensorflowjax

ragcomputer-visionnlp

Do you want to see your research directly impact how millions of customers discover, browse, and purchase products on Amazon — across Japan and the globe?

Amazon's Japan Store Tech team owns the science and technology behind cross-border shopping — product discovery, search relevance, personalization, and content experiences spanning dozens of marketplaces. We tackle problems at massive scale: multi-language signals, multi-marketplace data, and region-specific customer behaviors, all served at low latency to millions of daily shoppers.

We're looking for current Bachelor or Master students with a passion for applied science and machine learning to join us as an Applied Scientist in 2028 to shape the future of customer experiences at scale. For this position, our Japan Store Tech team is looking for students with a specialization in one or more of the following research areas: 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, and more!

As an Applied Scientist Intern, you'll develop novel models and algorithms, design and run experiments on live traffic, and own meaningful science contributions end-to-end. You'll also leverage and contribute to GenAI/LLM systems that power both customer-facing experiences and internal development tools.

If you want to kickstart your science career at global scale — solving real customer problems alongside talented scientists and engineers in a collaborative, international environment — this is the place to start.

Application & Assessment Deadline:
September 27, 2026
Selection Communication Begins:
October 5, 2026
Offer Extensions:
December 2026 – March 2027

Key job responsibilities
- Collaborate and communicate effectively with experienced cross-disciplinary Amazonians to design, develop, and deploy innovative machine learning models and scientific solutions that delight our customers, while participating in technical discussions to drive solutions forward.

- Develop and implement scalable machine learning models and algorithms to improve product discovery, search relevance, personalization, or other customer-facing experiences.

- Design and conduct experiments (offline and online) to validate hypotheses and measure the impact of proposed solutions.

- Analyze large-scale datasets to identify patterns, generate insights, and inform model design decisions.

- Leverage and contribute to the development of GenAI and LLM-powered tools to enhance customer experiences and development productivity while staying current with emerging technologies.

- Write clean, maintainable, production-quality code following best practices.

- Communicate research findings effectively through documentation, presentations, and technical papers.

- Work in an agile environment and collaborate closely with software engineers to bring science solutions from prototype to production.

Basic qualifications

- Currently enrolled in a Bachelor or Master program in Computer Science, Machine Learning, Statistics, Applied Mathematics, Electrical Engineering, or a related quantitative field.
- Programming experience in one or more of: Python, Java, C++, or equivalent.
- Coursework or research experience in one or more of: machine learning, deep learning, NLP, information retrieval, computer vision, statistics, or optimization.
- Strong foundation in data structures, algorithms, and mathematical/statistical modeling.
- Expected to graduate in 2028 and able to start from either April 1st or October 1st in 2028.

Preferred qualifications

- Publications or submitted papers in top-tier venues (e.g., NeurIPS, ICML, ACL, EMNLP, CVPR, KDD, WWW, RecSys, SIGIR).
- Hands-on experience building and deploying ML models on large-scale datasets.
- Experience with deep learning frameworks (e.g., PyTorch, TensorFlow, JAX).
- Familiarity with large language models, generative AI, or retrieval-augmented generation (RAG).
- Experience designing and analyzing A/B tests or causal inference experiments.
- Strong written and verbal communication skills; ability to present complex technical ideas clearly.
- Experience with cloud computing platforms (e.g., AWS).
- Proficiency in Japanese is a plus but not required; English fluency is required.

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.

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

RagComputer VisionNlpPyTorchTensorFlowJax

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
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?
What's a project where you used PyTorch hands-on?
Walk me through how you've used TensorFlow in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Rag, Computer Vision, Nlp, PyTorch, and TensorFlow. 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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