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Amazon
Amazon

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Applied Scientist II, Trustworthy Shopping Experience (TSE)

職種機械学習
経験ミドル級
勤務地Bengaluru, KA, India
勤務オンサイト
雇用正社員
掲載2週間前
応募する

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.

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Amazonについて

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+

従業員数

Seattle

本社所在地

$1.5T

企業価値

レビュー

10件のレビュー

3.4

10件のレビュー

ワークライフバランス

2.5

報酬

4.2

企業文化

3.0

キャリア

3.8

経営陣

2.7

65%

知人への推奨率

良い点

Great benefits and competitive pay

Learning and advancement opportunities

Good teamwork and colleagues

改善点

High pressure and long hours

Poor work-life balance

Toxic work culture and management issues

給与レンジ

4件のデータ

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件のレポート

$181,968

年収総額

基本給

-

ストック

-

ボーナス

-

$154,672

$209,264

面接レビュー

レビュー6件

難易度

4.0

/ 5

期間

21-35週間

体験

ポジティブ 0%

普通 17%

ネガティブ 83%

面接プロセス

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Technical Phone Screen

5

Technical Interview

6

Onsite/Virtual Interviews

よくある質問

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge