Applied Scientist, Personalization, Personalization

Benefits

Parental leave

6 weeks From the posting source checked Jun 20, 2026

Non-birth-parent leave

6 weeks From the posting source checked Jun 20, 2026

Family-building benefits

• Fertility benefits: Offered From the posting source checked Jun 20, 2026
• Adoption assistance: Offered From the posting source checked Jun 20, 2026
• Surrogacy assistance: Not verified

Mental health support

Offered From the posting source checked Jun 20, 2026

Relocation assistance

Not verified

Childcare support

Offered From the posting source checked Jun 20, 2026

Learning budget

Not verified

Verification

Source-linked checked Jun 7, 2026

Salary

Not verified

401(k) match

Reported from DOL Form 5500 industry filing (not employer-specific)

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

Not verified

Weekend work

Not verified

Company

Equity

Offered Verified - SEC 10-K source checked Jun 20, 2026

Application

Cover letter

Not verified

Assessment

Not verified

Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Applied Scientist, Personalization, Personalization Haifa, Haifa, ISR 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

Read the full description at www.amazon.jobs. FewerJobs shows a preview and links to the original posting.