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채용Tesla

Recruitment Lead

Tesla

Recruitment Lead

Tesla

Tokyo, Tokyo

·

On-site

·

Full-time

·

Today

ポジションの魅力
At Tesla, the Recruiter does more than fill roles — you’ll drive our talent strategy and connect our mission with the people who will bring it to life.

We don’t just hire for skills; we seek those who believe in a sustainable future and are ready to help change the world.

As a Recruiter entrusted with this important mission, you will work on the front lines to uncover top global talent, build high-performing teams, and accelerate the realization of Tesla’s goals.

Are you ready to be the one who discovers the changemakers of the future?
Position Description
The Recruiter at Tesla Japan is a talent strategist who connects top professionals with Tesla’s mission-driven teams, driving impactful hiring outcomes and supporting long-term growth.

Responsibilities

  • Act as a talent advisor to internal stakeholders, leveraging deep recruiting expertise to provide strategic recommendations on talent acquisition, including:
  • Designing effective sourcing strategies
  • Candidate evaluation methodologies
  • Driving data-informed hiring decisions
  • Manage end-to-end recruitment processes, ensuring timely and transparent communication with both hiring teams and candidates.
  • Effectively convey Tesla’s mission, values, and business operations to potential talent.
  • Evaluate candidates through a combination of technical and behavioral interview techniques, and apply non-traditional methods to identify top-tier talent.
  • Actively listen during candidate interactions, demonstrate strong negotiation skills, and maintain continuous engagement with candidates throughout the hiring journey, bringing creativity and initiative to the process.
  • Provide regular written and verbal updates on recruitment projects and pipeline status to hiring managers and leadership.
  • Deliver concise candidate evaluation summaries and recommendations.
  • Perform other duties as assigned by management based on evolving business needs.


  • Requirements
    【必須(MUST)】

  • Bachelor’s degree or higher. 10+ years of recruitment experience, preferably in fast-paced environments with evolving business and product demands.
  • Fluency in English, both written and spoken.
  • Strong proficiency in both traditional and modern sourcing channels
  • Proven ability to build strong relationships with key decision-makers and influencers across the organization.


  • 【歓迎(WANT)】

  • Resonated with Tesla’s startup culture and possess an entrepreneurial mindset.
  • Demonstrated success in high-volume hiring for roles requiring strong technical and interpersonal skills.
  • Advanced understanding of organizational dynamics and experience navigating complex, matrixed environments.
  • Strong self-discipline and the ability to learn independently and continuously improve.
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    모의 지원자 수

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    Tesla 소개

    Tesla

    Tesla

    Public

    A financial leasing taxi company that provides vehicles to customers

    140,000+

    직원 수

    Ciudad De Panamá

    본사 위치

    $800B

    기업 가치

    리뷰

    3.1

    5개 리뷰

    워라밸

    1.5

    보상

    1.2

    문화

    1.3

    커리어

    1.8

    경영진

    1.1

    15%

    친구에게 추천

    장점

    Strong financial performance

    Revenue growth

    Company achieving targets

    단점

    Poor compensation and raises below inflation

    Union-busting and anti-labor practices

    Unpaid work demands and wage theft

    연봉 정보

    1,397개 데이터

    Junior/L3

    Mid/L4

    Senior/L5

    Junior/L3 · ASSOCIATE MANAGER, LOGISTICS

    2개 리포트

    $131,600

    총 연봉

    기본급

    $114,400

    주식

    -

    보너스

    -

    $131,600

    $131,600

    면접 경험

    4개 면접

    난이도

    3.5

    / 5

    소요 기간

    14-28주

    경험

    긍정 0%

    보통 75%

    부정 25%

    면접 과정

    1

    Application Review

    2

    Recruiter Screen

    3

    Technical Phone Screen

    4

    Take-home Assignment

    5

    Panel Interview

    6

    Offer

    자주 나오는 질문

    Coding/Algorithm

    Technical Knowledge

    Behavioral/STAR

    System Design

    Machine Learning Concepts