Applied Scientist, Personalization, Personalization

Amazon is hiring an Applied Scientist, Personalization, Personalization in Tel Aviv, Israel. Level rates it ; you can apply on Amazon.

Level's read on this job

Builds AI ●●●● is how Level rates this applied scientist role: the job is building AI systems.
The posting mentions Python, C and Java among its tools and skills.
Level classes it as a senior role.
Amazon has 20,000+ other open roles on Level.

AI in this role

Design and deploy large-scale machine learning and LLM-powered personalization systems for Amazon customers.

PythonCJava

nlpmachine-learningllmsrecommendation-systemsinformation-extractiondistributed-systems

We are seeking an Applied Scientist to help build Amazon’s next-generation customer memory and personalization systems.

Are you interested in building systems that move beyond reacting to customer behavior, to actually understanding and remembering it over time? Our team is building Amazon’s customer memory layer – a system that extracts, curates, and reasons over customer knowledge to power next-generation personalization. This includes transforming noisy, unstructured signals into durable, high-quality representations of customer preferences, intents, and life events, and using them in real time to improve customer experiences.

We are part of Amazon’s Personalization organization, a high-performing group that leverages large-scale machine learning, generative AI, and distributed systems to deliver highly relevant customer experiences.
We tackle challenging problems at the intersection of information extraction, knowledge representation, LLM reasoning, and recommendation systems. Our systems operate under real-world constraints of scale, latency, and quality, requiring careful tradeoffs between precision, recall, and responsiveness. This team plays a central role in defining how Amazon understands its customers, and how that understanding is applied across the shopping experience.

As an Applied Scientist, you will design and build ML and LLM-powered solutions for Amazon's customer memory and
personalization systems. You will work on how customer knowledge is extracted, validated, and applied in production
systems.

You will own the end-to-end delivery of ML solutions, from problem formulation and modeling to offline and online experimentation, and production deployment at scale. You will deliver high-quality, scalable systems that power customer-facing experiences.
You will drive work across areas such as fact extraction, memory quality and lifecycle, temporal reasoning, and grounded personalization, while navigating tradeoffs between quality, latency, and coverage.
You will collaborate closely with engineering and product teams to translate research into measurable customer impact.

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

Basic qualifications

- Knowledge of programming languages such as C/C++, Python, Java or Perl
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience communicating clearly and concisely with leadership, stakeholders, and cross-functional teams

Preferred qualifications

- Experience in building and launching deep learning and machine learning models for business applications
- Solid knowledge of big data and cloud technologies (e.g., Spark, AWS, etc.)
- Experience with information retrieval, recommender systems, natural language processing, and/or personalization algorithms
- Publications at top Web, Machine Learning, Natural Language Processing conferences such as KDD, ICML, NeurIPS, ACL, EMNLP, etc.

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 Scientist, Personalization, 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.

●●●● 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. Read the description and count the AI tools, models and workflows it names.

Key facts

Applied Scientist, Personalization, Personalization is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in IL, Tel Aviv.
The job was posted on June 22, 2026.
Seniority: senior.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

NLPMachine learningLLMsRecommendation SystemsInformation ExtractionDistributed SystemsPythonC

Also common in applied scientist jobs at this level

Reinforcement learning TensorFlow Computer vision scikit-learn PyTorch Fine-tuning

Pay

Typical applied scientist job: $172k

This posting doesn't list pay. Use the typical applied scientist figure when pay comes up. See applied scientist jobs

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 llms was part of your work. What did you do?
Tell me about a project where recommendation systems was part of your work. What did you do?
Tell me about a project where information extraction was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: NLP, Machine learning, LLMs, Recommendation Systems, and Information Extraction. 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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