refresh

지금 많이 보는 기업

지금 많이 보는 기업

Amazon
Amazon

Work hard. Have fun. Make history.

Applied Scientist II, Trustworthy Shopping Experience (TSE)

직무머신러닝
경력미들급
위치Bengaluru, KA, India
근무오피스 출근
고용정규직
게시2주 전
지원하기

Are you passionate about solving complex business problems at scale through Generative AI? Do you want to build intelligent systems that reason, act, and learn from minimal supervision? Are you excited about taking innovative AI solutions from proof-of-concept to production? If so, we have an exciting opportunity for you on Amazon's Trustworthy Shopping Experience (TSE) team.

At TSE, our vision is to guarantee customers a worry-free shopping experience by earning their trust that the products they buy are safe, authentic, and compliant with regulations and policy. We give customers confidence that Amazon stands behind every product and will make it right in the rare chance anything goes wrong. We do this in close partnership with our selling partners and empower them with best-in-class tools and expertise required to offer a high-quality selection of compliant products that customers trust.

As an Applied Scientist, you will lead the development of next Gen AI solutions to automate complex manual investigation processes at Amazon scale. You will work on some of the most fascinating challenges in applied AI—building systems that reason and act autonomously, learn rich representations from structured and relational data without extensive labels, adapt rapidly from limited examples, improve through feedback and interaction, seamlessly connect visual and textual understanding, and compress complex model capabilities into efficient, deployable systems. Your innovations will deliver significant impact to cost-of-serving customers while maintaining the highest standards of trust and safety.

This role offers end-to-end ownership—from initial research and proof-of-concept through production
deployment. You will see your innovations serving hundreds of millions of customers within months, not years.

  • Key job responsibilities
  • Design and build expertise agentic AI systems with multi-step reasoning, autonomous task execution, and multimodal intelligence with capabilities to handle feedback with memory mechanisms.
  • Productionize large scale models built on top of SFT (Supervised Finetuning) and RFT (Reinforced fine tuning) approaches, few shot approaches based on multimodal datasets
  • Build novel production ready Deep and conventional ML solutions to aid the multiple potential automation requirements
  • Identify customer and business problems at project level; invent or extend state-of-the-art approaches for complex workflows involving unstructured text, documents, images, and relational data
  • Author or co-author research papers for peer-reviewed venues; serve as PC member at conferences when aligned with business needs
  • Prototype rapidly, iterate based on feedback, and deliver components at SDE I+ level that integrate directly into production-scale systems
  • Engineer efficient systems balancing model capability, deployment cost, and resource usage; write significant code demonstrating technical excellence and maintainability
  • Scrutinize algorithm and software performance for improvements; resolve root causes leaving systems more maintainable
  • Contribute to tactical and strategic planning—team goals, priorities, and roadmaps—while providing architectural guidance for AI systems
  • Participate in engineering best practices with rigorous peer reviews; communicate design decisions clearly and participate in science reviews
  • Train new teammates on component construction and integration; mentor less experienced scientists and participate in hiring processes

About the team
Investigation technology Product team in TSE is responsible for the human-in-the-loop products and technology used in the risk investigations at Amazon. The team is also responsible for reducing the cost of performing the investigations, by automating wherever possible and optimizing the experience where manual interventions are needed. The team leverages state-of-the art technology and GenAI to deliver the products and associated goals.

Basic Qualifications

  • 3+ years of building models for business application experience
  • PhD, or Master's degree and 3+ years of CS, CE, ML or related field experience
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals
  • 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

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.

전체 조회수

0

전체 지원 클릭

0

전체 Mock Apply

0

전체 스크랩

0

Amazon 소개

Amazon

Amazon

Public

Amazon.com, Inc. is an American multinational technology company engaged in e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence.

10,001+

직원 수

Seattle

본사 위치

$1.5T

기업 가치

리뷰

10개 리뷰

3.4

10개 리뷰

워라밸

2.5

보상

4.2

문화

3.0

커리어

3.8

경영진

2.7

65%

지인 추천률

장점

Great benefits and competitive pay

Learning and advancement opportunities

Good teamwork and colleagues

단점

High pressure and long hours

Poor work-life balance

Toxic work culture and management issues

연봉 정보

4개 데이터

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

0개 리포트

$181,968

총 연봉

기본급

-

주식

-

보너스

-

$154,672

$209,264

면접 후기

후기 6개

난이도

4.0

/ 5

소요 기간

21-35주

경험

긍정 0%

보통 17%

부정 83%

면접 과정

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Technical Phone Screen

5

Technical Interview

6

Onsite/Virtual Interviews

자주 나오는 질문

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge