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Applied Scientist, Central Seller Fulfillment
We are seeking a stellar Machine Learning scientist who has experience developing and shipping large scale ML products with visible customer impact. We would prefer if your previous work has been in developing scalable Agentic, RL or forecasting systems. Strong academic background in Statistics, Machine Learning & Deep Learning is required with Tier -1 publications being a plus.
- Master’s degree in CS or ML related fields
- Scientist/Tech Lead creating and shipping impactful ML products.
- Ability to write clear, structured and modularized code in Python.
- Expertise in Deep Learning frameworks such as Tensorflow, Keras and Pytorch & Agentic frameworks such as Lang Chain, Crew AI etc.
- Industry experience designing complex scalable AI systems.
- Experience and technical expertise across various science domains. Crucial ones being statistics, deep & machine learning.
- Experience creating data pipelines & proficient in querying data from Spark/HIVE/Redshift/other large scale data warehousing platforms.
- Expert in distilling informal customer requirements into problem definitions, dealing with ambiguity and formulating ML products to solve these problems.
Key job responsibilities
In this position, you will be a key contributor (with direct leadership visibility) building, productionizing (real & batch) and measuring impact of state of the art personalized Gen AI systems for Amazon global selling partners and contribute to Amazon wide research in this area in the form of publications and white papers. You will work with global leaders and teams across time zones on a regular basis.
About the team
Millions of Sellers list their products for sale on the Amazon Marketplace. Sellers are a critical part of Amazon’s ecosystem to deliver on our vision of offering the Earth’s largest selection and lowest prices. In this ecosystem our team plays a critical role in enabling Sellers across EU5, China, Japan, Australia, Brazil and Turkey to make their Selection available to customers globally and deliver the experience they have come to expect from Amazon.
We help independent sellers compete against our first-party business by investing in and offering them the very best selling tools we could imagine and build. We are pushing the boundaries of these machine learning tools in areas of Agentic, recommendation and forecasting systems to help our sellers sell more and across borders.
Basic Qualifications
- Experience programming in Java, C++, Python or related language
- Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse
Preferred Qualifications
- Experience implementing algorithms using both toolkits and self-developed code
- Have publications at top-tier peer-reviewed conferences or journals
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.
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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
10条评价
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%
推荐率
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
L6
M3
M4
M5
M6
Mid/L4
Principal/L7
Senior/L5
Staff/L6
Director
L3
L4
L5
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
面试评价
6条评价
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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