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트렌딩 기업

트렌딩 기업

채용

채용Google

Software Engineering Manager, Agentic AI

Google

Software Engineering Manager, Agentic AI

Google

·

On-site

·

Full-time

·

1w ago

  • Propose, design, and develop new functionality to create a robust automation system for Google's supply chain and data center deployments.

  • Ensure our production-facing Service Level Objectives (SLOs) are met while continuously adding new capabilities to our software.

  • Influence, review, and represent our team across leadership forums.

  • Leverage Agentic AI to improve automation and reduce turnaround times.

  • Lead and drive long-term software architecture and roadmaps to build and deploy Agentic AI applications to manage rack deployments of GPUs, TPUs, and networking racks.

  • Bachelor’s degree, or equivalent practical experience.

  • 8 years of experience in software development.

  • 3 years of experience in a technical leadership role.

  • 2 years of experience in a people management or team leadership role.

  • Experience with distributed systems.

총 조회수

0

총 지원 클릭 수

0

모의 지원자 수

0

스크랩

0

Google 소개

Google

Google

Public

Google specializes in internet-related services and products, including search, advertising, and software.

10,001+

직원 수

Mountain View

본사 위치

$1,700B

기업 가치

리뷰

3.7

25개 리뷰

워라밸

3.8

보상

4.2

문화

3.4

커리어

3.9

경영진

2.8

68%

친구에게 추천

장점

Excellent compensation and benefits

Smart and talented colleagues

Great perks and work flexibility

단점

Management and leadership issues

Bureaucracy and slow processes

Constantly changing priorities and reorganizations

연봉 정보

57,502개 데이터

Junior/L3

L3

L4

L5

L6

L7

L8

Mid/L4

Principal/L7

Senior/L5

Staff/L6

Director

Junior/L3 · Data Scientist L3

0개 리포트

$176,704

총 연봉

기본급

-

주식

-

보너스

-

$150,298

$203,110

면접 경험

9개 면접

난이도

3.4

/ 5

소요 기간

14-28주

합격률

44%

경험

긍정 0%

보통 56%

부정 44%

면접 과정

1

Application Review

2

Online Assessment/Technical Screen

3

Phone Screen

4

Onsite/Virtual Interviews

5

Team Matching

6

Offer

자주 나오는 질문

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Product Sense