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Tesla
Tesla

Accelerating the world's transition to sustainable energy.

System Integration/Validation Engineer (m/w/d) – Off-Board Vehicle Charging

职能工程
级别中级
地点Freiburg Im Breisgau
方式现场办公
类型全职
发布今天
立即申请

必备技能

Python

What To Expect
Please submit your CV in English in order to be considered for the role.

To ensure Tesla's overall mission of accelerating the world's transition to sustainable abundance, the Tesla Energy team is leveraging its world-class expertise in power electronics, vertical integration, firmware development, and global large-scale deployments to deliver reliable and scalable charging products.

Tesla Energy is looking for an engineer who is passionate about hardware/firmware integration and validation to drive the world’s transition to a sustainable future. We emphasise collaboration in our work and always encourage each other to have fun whilst we change the world together. 

What You'll Do

  • Perform end-to-end hardware and firmware integration across Tesla’s charging systems
  • Lead validation of hardware controls and system-level functionality for new Supercharger products (including hardware design/control validation, board debugging, root-cause analysis and hardware/firmware regression testing)
  • Develop and execute test plans to validate functions, features, and system performance
  • Work closely with cross-functional teams to ensure functional, performance, and safety requirements are achieved
  • Conduct failure analysis to understand root cause and drive resolution for failures originating in testing and from the field
  • Drive continuous improvement in design and performance by providing direct and accurate feedback to design owners
  • Debug and fix hardware and firmware issues on complex, high-power/high-voltage products
  • Provide hands-on support with hardware bring-up and prototyping


  • What You'll Bring

  • Degree in Electrical Engineering or equivalent degree/experience
  • 3+ years of experience in electronic hardware integration and/or firmware integration
  • In-depth understanding of HV power electronic components (active and passive) and expertise in developing associated characterisation/validation testing methods
  • Strong understanding of device characteristics, gate driving circuit design, thermal design, magnetics design (inductors, transformers), power electronics topologies (AC/DC, DC/DC, and DC/AC), modulation schemes, and control theories
  • Proficiency in Altium or other schematic entry and PCB layout software
  • Experience with Python, C, C++
  • Proficiency with basic EE tools (scopes, network analysers, power supplies, electronic loads, CAN tools)
  • Exceptional analytical problem-solving skills, able to identify and surface issues within complex hardware/software systems
  • Effective communicator and excellent attention to detail
  • Experience with inverters, batteries, controls, and industrial grid codes is preferred


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    关于Tesla

    Tesla

    Tesla

    Public

    A financial leasing taxi company that provides vehicles to customers

    140,000+

    员工数

    Ciudad De Panamá

    总部位置

    $800B

    企业估值

    评价

    10条评价

    3.8

    10条评价

    工作生活平衡

    2.3

    薪酬

    4.0

    企业文化

    3.2

    职业发展

    4.1

    管理层

    2.8

    65%

    推荐率

    优点

    Innovative projects and cutting-edge technology

    Great team and supportive colleagues

    Good compensation and benefits

    缺点

    Long hours and poor work-life balance

    High pressure and tight deadlines

    Management issues and high expectations

    薪资范围

    1,403个数据点

    Junior/L3

    Mid/L4

    Junior/L3 · Associate Analyst

    2份报告

    $94,875

    年薪总额

    基本工资

    $82,500

    股票

    -

    奖金

    -

    $92,000

    $97,750

    面试评价

    4条评价

    难度

    3.5

    / 5

    时长

    14-28周

    体验

    正面 0%

    中性 75%

    负面 25%

    面试流程

    1

    Application Review

    2

    Recruiter Screen

    3

    Technical Phone Screen

    4

    Take-home Assignment

    5

    Panel Interview

    6

    Offer

    常见问题

    Coding/Algorithm

    Technical Knowledge

    Behavioral/STAR

    System Design

    Machine Learning Concepts