Level

Amazon

Applied Science Manager, Personalization

AI in this role

Applied Science Manager leading a team building next-generation e-commerce personalization systems powered by Large Language Models.

pythonllms
fine-tuningnlpmachine-learningrecommendation-systemsinformation-retrievalpeople-management
Are you a scientist interested in pushing the state of the art in Information Retrieval, Large Language Models and Recommendation Systems? Are you interested in innovating on behalf of millions of customers, helping them accomplish their every day goals? Do you wish you had access to large datasets and tremendous computational resources? Do you want to join a team of capable scientist and engineers, building the future of e-commerce? Answer yes to any of these questions, and you will be a great fit for our team at Amazon.

Our team is part of Amazon’s Personalization organization, a high-performing group that leverages Amazon’s expertise in machine learning, generative AI, large-scale data systems, and user experience design to deliver the best shopping experiences for our customers. Our team is building next-generation personalization systems powered by Large Language Models. We are tackling novel research challenges to help customers discover products they'll love - at Amazon scale and latency requirements. We are a team uniquely placed within Amazon, to have a direct window of opportunity to influence how customers will think about their shopping journey in the future.

As an Applied Science Manager, you will lead a team of scientists working at the frontier of LLM-based personalization. You will set the technical vision, drive the research agenda, and ensure your team delivers production-ready solutions. You will hire, mentor, and develop world-class scientists while fostering a culture of innovation and scientific rigor. You will partner closely with engineering and product teams to translate ambitious research into customer-facing impact, and represent your team's work to senior leadership.

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

Basic qualifications

- PhD or equivalent research experience, or Master's degree
- 3+ years of scientists or machine learning engineers management experience
- 3+ years of building machine learning models for business application experience
- Experience leading applied research in one or more of: Recommendation Systems, Information Retrieval, NLP, or Large Language Models
- Demonstrated ability to think strategically, communicate effectively (written and verbal) with senior leadership, and drive cross-team collaboration

Preferred qualifications

- Experience working on recommender systems or personalization within search, e-commerce, shopping, advertising or other related fields
- Experience with LLM training, fine-tuning, or adaptation (e.g., tokenizer modification, domain adaptation)

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.

How we rate this

Applied Science Manager, Personalization 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.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ 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

Fine TuningNLPMachine LearningRecommendation SystemsInformation RetrievalPeople ManagementPythonLLMs

Questions you could be asked

  1. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  2. What NLP problem have you worked on, and how did you measure whether it actually worked?
  3. Tell me about a project where machine learning was part of your work. What did you do?
  4. Tell me about a project where recommendation systems was part of your work. What did you do?
  5. Tell me about a project where information retrieval was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: Fine Tuning, NLP, Machine Learning, Recommendation Systems, and Information Retrieval. 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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