Senior Applied Scientist, International Seller Services

AI in this role

Provide scientific and technical leadership in developing LLM-based solutions for global e-commerce challenges.

tensorflowscikit-learnpythonnlpllm

nlpmachine-learningnatural-language-processingtechnical-leadershipdata-analysis

Are you fascinated by the power of Natural Language Processing (NLP) and Large Language Models (LLM) to transform the way we interact with technology? Are you passionate about applying advanced machine learning techniques to solve complex challenges in the e-commerce space? If so, Amazon's International Seller Services team has an exciting opportunity for you as Sr Applied Scientist.

At Amazon, we strive to be Earth's most customer-centric company, where customers can find and discover anything they want to buy online. Our International Seller Services team plays a pivotal role in expanding the reach of our marketplace to sellers worldwide, ensuring customers have access to a vast selection of products.

As Sr Applied Scientist, you will join a talented and collaborative team that is dedicated to driving innovation and delivering exceptional experiences for our customers and sellers. You will be part of a global team that is focused on acquiring new merchants from around the world to sell on Amazon’s global marketplaces around the world. The position is based in Seattle but will interact with global leaders and teams in Europe, Japan, China, Australia, and other regions.

Join us at the Central Science Team of Amazon's International Seller Services and become part of a global team that is redefining the future of e-commerce. With access to vast amounts of data, cutting-edge technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way sellers engage with our platform and customers worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce.

Please visit https://www.amazon.science for more information

Key job responsibilities
Provide scientific and technical leadership in the design and development of scalable LLM-based solutions that address complex, ambiguous language challenges across the International Seller Services domain — setting the technical direction and raising the science bar for the team.

Partner strategically with cross-functional leaders — software engineers, data scientists, and product managers — to shape project vision, define success metrics, and drive the delivery of high-impact solutions from concept to production.

Lead deep, rigorous data analysis to surface insights, uncover patterns, and translate them into actionable recommendations that measurably improve seller performance and customer experiences across global marketplaces.

Drive innovation by researching, evaluating, and championing state-of-the-art NLP and LLM techniques, and by influencing the adoption of best practices that advance the accuracy, efficiency, and scalability of our language systems.

Serve as a trusted technical advisor — communicating complex scientific concepts with clarity to both technical and executive stakeholders, and guiding decision-making on solution trade-offs and their broader business impact.

Mentor and elevate fellow scientists and engineers, fostering a culture of scientific excellence, peer review, and continuous learning across the organization.

A day in the life
Set the scientific direction - architect and lead the development of scalable LLM and NLP solutions that tackle the most complex, ambiguous language challenges in seller acquisition, content generation, and catalog understanding

Turn frontier research into production reality - champion state-of-the-art techniques, prototype ambitious ideas, and partner with engineers to ship science that operates reliably at massive scale

Harness data at global scale - leverage some of the richest e-commerce datasets in the world to uncover deep insights and translate them into actionable strategy that measurably moves the business

Influence beyond your team - partner with leaders across product, engineering, and science to shape roadmaps, define success metrics, and align solutions that generalize across Europe, Japan, China and beyond

Solve problems that matter - frame ambiguous business challenges into well-scoped science problems that directly serve sellers and customers worldwide

Raise the bar - mentor and elevate fellow scientists and engineers, lead design and peer reviews, and foster a culture of scientific rigor and continuous learning

Be a trusted technical voice - communicate complex concepts with clarity to both technical teams and senior executives, guiding key decisions on trade-offs and long-term impact

Basic qualifications

- 5+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning

Preferred qualifications

- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Knowledge of programming languages such as C/C++, Python, Java or Perl

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 base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually

How we rate this

Senior Applied Scientist, International Seller Services 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. 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

NLPMachine LearningNatural Language ProcessingTechnical LeadershipData AnalysisTensorFlowscikit-learnPython

Questions you could be asked

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 natural language processing was part of your work. What did you do?
Tell me about a project where technical leadership 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?

Adapt your resume

List these exact terms on your resume: NLP, Machine Learning, Natural Language Processing, Technical Leadership, and Data Analysis. 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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