채용

Applied Scientist - Analytics AI, AI Core Infra - AI Analytics and Insights
Seattle, WA, USA
·
On-site
·
Full-time
·
4w ago
필수 스킬
SQL
Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering a collection of advertising solutions that drive product discovery and sales. We deliver billions of ad impressions every single day on behalf of our advertisers. You'll work with us to help our Advertising teams make sense of the torrent of data produced by the advertising lifecycle.
We are using SOTA generative AI to help teams generate insights faster based on our massive data lake. You will need to invent new techniques for metrics retrieval and SQL generation to ensure we're retrieving accurate and trusted data. You'll create feedback loops to ensure our solution is constantly evaluating itself and improving.
Being that this is for a conversational AI position, here is what our bot replied when we prompted it for a job description of who should help build it:
Role Overview:
We are looking for an exceptional applied scientist to join our team building Spektr Bot, a conversational AI assistant that helps data engineers and analysts with their workflows. You will work closely with engineers and product managers to design, implement, and optimize natural language processing models like intent classification, named entity recognition, question answering, etc. that enable our Ads chatbot to understand user requests and have natural conversations.
Responsibilities:
Study and understand data engineering and analytics workflows to design the right conversational experiences
Research, design, and develop NLP/NLU models for intent classification, entity extraction, sentiment analysis etc.
Continuously improve models through techniques like active learning, transfer learning etc.
Optimize models for metrics like precision, recall, latency, interpretability etc.
Implement models within overall bot architecture and integrate with backend systems
Collaborate with engineers to productionize and monitor models
Stay up-to-date on latest advancements in conversational AI research, specifically in LLMs (multi-agent, chain of thought, autonomous agents)
Be familiar with optimizing retrievers in RAG architectures.
Key job responsibilities
You will test multiple foundational models and fine tune when appropriate. You will create feedback loops that will evaluate performance and improve our systems. You will optimize prompts for better responses from our LLMs. You will build tools to auto-curate metadata using LLMs.
A day in the life
You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.
About the team
We have a small scrappy team carved out from a large Ads wide data lake team. We are swimming in petabytes of data that we help the organization make sense of. Our team's mission is to help anyone in the Ads org find the data they need using only natural language. We are a supportive and collaborative team who iterates quickly and shares in each others' successes.
Basic Qualifications
- 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
- 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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Amazon 소개

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+
직원 수
Seattle
본사 위치
$1.5T
기업 가치
리뷰
2.9
10개 리뷰
워라밸
2.8
보상
3.7
문화
2.5
커리어
2.3
경영진
2.1
35%
친구에게 추천
장점
Good pay and compensation
Strong benefits package
Flexible scheduling options
단점
Poor management and leadership
Limited growth and promotion opportunities
High stress and demanding work environment
연봉 정보
4개 데이터
L2
L3
L4
L5
L6
L2 · Data Analyst L2
0개 리포트
$108,330
총 연봉
기본급
$43,332
주식
$54,165
보너스
$10,833
$75,831
$140,829
면접 경험
10개 면접
난이도
3.7
/ 5
소요 기간
21-35주
합격률
20%
경험
긍정 10%
보통 10%
부정 80%
면접 과정
1
Application Review
2
Recruiter Screen
3
Online Assessment
4
Technical Phone Screen
5
Onsite/Virtual Loop
6
Team Matching
7
Offer
자주 나오는 질문
Coding/Algorithm
System Design
Behavioral/STAR
Leadership Principles
Technical Knowledge
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