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About This Role
DESCRIPTION
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Are you interested in big data, machine learning, LLM, and product recommendations? If so, Amazon's Personalization team might be the right place for you.
About our organization:
We are part of Amazon’s Personalization organization, a high\-performing group with a huge impact on hundreds of millions of customers, innovating at the intersection of customer experience, machine learning, and large\-scale distributed systems. We run global experiments and our work has revolutionized e\-commerce with features such as "Compare with similar items", "Keep shopping for ...", “Customers who bought this item also bought”, and, “Frequently bought together” among others.
Amazon’s internal surveys regularly recognize us as one of the best organizations to work for in the company, with visible high\-impact work, low operational load, respectful work\-life balance, and continuous opportunity to learn and grow.
About you:
You are an Applied Scientist who loves big data and passionate about improving customer shopping experience by inventing and applying state\-of\-art technologies (e.g., LLM, Machine Learning, NLP, and Computer Vision) to build the next\-generation product recommendation engine for Amazon. You have an entrepreneurial spirit, know how to deliver, are deeply technical and highly innovative. You work closely with software engineers to put algorithms into production. You also work in partnership with teams across Amazon to create enormous benefits for our customers. You will have an opportunity to make an enormous impact on the design, architecture, and implementation of products used every day by people you know.
Key job responsibilities
Use machine learning and analytical techniques to create scalable solutions for business problems
Analyze and extract relevant information from large amounts of Amazon's historical business data to help automate and optimize key processes
Design, development and evaluation of highly innovative models for predictive learning
Work closely with software engineering teams to drive model implementations and new feature creations
Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
Research and implement novel machine learning and statistical approaches
Mentor junior scientists; review their work and provide feedback
About the team
Our mission is to delight every Amazon customer with a personalized shopping experience. We achieve our mission through investments in large\-scale machine learning and distributed system solutions with the purpose of delivering the future of shopping on Amazon. Our solutions help customers explore product categories, discover high quality products that meet their needs, and provide most relevant information to help customers make confident shopping decisions. We are seeking an Applied Scientist to make step function improvements in creating a delightful shopping experience.
BASIC QUALIFICATIONS
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- 3\+ years of building models for business application experience
- PhD, or Master's degree and 4\+ years of CS, CE, ML or related field experience
- Experience programming in Java, C\+\+, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high\-performance computing
PREFERRED QUALIFICATIONS
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- Experience using Unix/Linux
- Experience in professional software development
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 \- 142,800\.00 \- 193,200\.00 USD annually
Salary Context
This $142K-$193K range is below the median for Research Scientist roles in our dataset (median: $196K across 70 roles with salary data).
Role Details
About This Role
Research Scientists push the boundaries of what AI can do. They design experiments, develop novel architectures, publish papers, and translate research breakthroughs into production capabilities. This is where the fundamental advances happen, from attention mechanisms to diffusion models to reasoning chains.
The work is intellectually demanding and often ambiguous. You might spend months on an approach that doesn't pan out. The best research scientists combine deep mathematical intuition with engineering pragmatism. They know when to go deep on theory and when to run experiments. They read papers voraciously and can spot incremental contributions from genuine breakthroughs.
Across the 3,057 AI roles we're tracking, Research Scientist positions make up 3% of the market. At Amazon.com, this role fits into their broader AI and engineering organization.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
What the Work Looks Like
A typical week includes: reading and discussing recent papers with your team, designing and running experiments on multi-GPU clusters, analyzing results and iterating on hypotheses, writing up findings for internal review or publication, and collaborating with engineering teams to productionize promising results. The ratio of thinking to coding is higher than in engineering roles.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
Skills Required
PhD strongly preferred for most roles. Deep expertise in a specific area (NLP, computer vision, reinforcement learning, multimodal) is expected. PyTorch is the standard. Publication track record matters. Strong mathematical foundations in linear algebra, probability, optimization, and information theory are assumed.
Beyond the fundamentals, companies value experience with large-scale distributed training, novel architecture design, and the ability to bridge theory and practice. Understanding of current frontier topics (reasoning, multimodal, long-context, alignment) is essential. Code quality matters more than many researchers expect. Labs want researchers who can implement their ideas cleanly.
Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
Compensation Benchmarks
Research Scientist roles pay a median of $223,400 based on 224 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($168K) sits 25% below the category median. Disclosed range: $142K to $193K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Amazon.com AI Hiring
Amazon.com has 71 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, AI Product Manager, Data Scientist. Positions span Seattle, WA, US, New York, NY, US, Sunnyvale, CA, US. Compensation range: $101K - $300K.
Location Context
AI roles in Seattle pay a median of $227,750 across 1,010 tracked positions. That's 14% above the national median.
Career Path
Common paths into Research Scientist roles include PhD Student, Research Engineer, Postdoc.
From here, career progression typically leads toward Research Lead, Distinguished Scientist, VP of Research.
The PhD is the entry point for most paths. Choose your advisor and research area carefully since they'll define your first industry position. Publish consistently, contribute to open-source projects in your area, and build relationships at conferences. Industry research offers better compensation and compute resources than academia, but the pressure to show product impact is real.
What to Expect in Interviews
Research interviews are multi-stage: a research talk (present your best paper), technical deep-dives on your methodology, and often a 'research proposal' exercise where you design an experiment to test a hypothesis. Coding rounds test implementation ability alongside theoretical knowledge. Be prepared to implement a paper from scratch and discuss the design choices the authors made. Strong candidates can critique papers constructively and identify gaps in experimental methodology.
When evaluating opportunities: Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
AI Hiring Overview
The AI job market has 3,057 open positions tracked in our dataset. By seniority: 94 entry-level, 1,467 mid-level, 1,148 senior, and 348 leadership roles (Director, VP, C-Level). Remote roles make up 17% of the market (513 positions). The remaining 2,528 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
The AI Job Market Today
The AI job market spans 3,057 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,189), Data Scientist (233), AI Software Engineer (195). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (94) are outnumbered by mid-level (1,467) and senior (1,148) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 348 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 17% of all AI roles (513 positions), with 2,528 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,566 postings), Aws (974 postings), Azure (725 postings), Rag (683 postings), Gcp (597 postings), Prompt Engineering (472 postings), Pytorch (461 postings), Claude (447 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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