招聘
We are building the next generation of personalized shopping experiences at Amazon through deep understanding of hundreds of millions of customers, billions of products and intricate context.
We set out to create a personal shopping partner that is knowledgeable, understands your preferences, and helps you find the right solution for your needs.
Key job responsibilities
As an Applied Scientist on the team, you will help lead science innovation across multiple teams. You will solve a breadth of problems, ranging from developing state of the art thinking-LLM-based techniques to reason about customers and products; creating semantic representations of products, customers, and context; developing cross-attentive LLM rankers ; training multi-objective ranking and optimization systems; deploying large scale real-time AI systems. You will build tech used by teams across Amazon, while also having a direct connection to hundreds of millions of customers using Amazon to shop. You will improves lives of customers through improvements on offline benchmarks as well as online experiments.
About the team
We build new experiences powered by Generative models and our new LLM-based AI stack, that provides personalized and multi-modal Natural Language interface to Amazon's billions of products for both hundreds of millions of retail customers as well as internal teams building AI experiences such as Rufus, Alexa+, Interests AI, and multiple confidential initiatives.
Come join us on this journey!
Basic Qualifications
- 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
- 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
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关于Amazon

Amazon
PublicAmazon.com, Inc. is an American multinational technology company engaged in e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence.
10,001+
员工数
Seattle
总部位置
$1.5T
企业估值
评价
3.4
10条评价
工作生活平衡
2.5
薪酬
4.2
企业文化
3.0
职业发展
3.8
管理层
2.7
65%
推荐给朋友
优点
Great benefits and competitive pay
Learning and advancement opportunities
Good teamwork and colleagues
缺点
High pressure and long hours
Poor work-life balance
Toxic work culture and management issues
薪资范围
4个数据点
Junior/L3
L2
L3
L4
L5
L6
M3
M4
M5
M6
Mid/L4
Principal/L7
Senior/L5
Staff/L6
Director
Junior/L3 · Data Scientist L4
0份报告
$181,968
年薪总额
基本工资
-
股票
-
奖金
-
$154,672
$209,264
面试经验
6次面试
难度
4.0
/ 5
时长
21-35周
体验
正面 0%
中性 17%
负面 83%
面试流程
1
Application Review
2
Recruiter Screen
3
Online Assessment
4
Technical Phone Screen
5
Technical Interview
6
Onsite/Virtual Interviews
常见问题
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
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