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职位Tesla

Validation Engineer, Body Controls, Vehicle Software

Tesla

Validation Engineer, Body Controls, Vehicle Software

Tesla

Palo Alto, California

·

On-site

·

Full-time

·

Today

必备技能

Python

Go

What To Expect
The Body Controls Validation team ensures the quality and reliability of firmware, sensing, and control systems that bring Tesla vehicles to life. From powered doors and seats to liftgates, windows, wipers, mirrors, and sophisticated interior and exterior lighting systems, we validate the embedded software that powers every tactile interaction customers have with their vehicles.

You'll be at the forefront of self-driving technology, validating user behaviors and interactions for our next-generation fully autonomous vehicle platform. Your work ensures computer vision systems continuously see the world through clean lenses, doors respond seamlessly and safely to user interactions, and interior lighting creates customizable ambiance. You'll also contribute to scaling our Semi truck program, tackling unique validation challenges in pneumatic systems, driver comfort, high-volume manufacturing, and more.

This is a small, passionate, fast-moving team where your validation work directly protects customer safety and experience. Join a group of deeply knowledgeable Engineers who build some of the most comprehensive test infrastructure in the industry, using cutting-edge automation tools and practices to deliver exceptional reliability and performance.

What You'll Do

  • Design exhaustive automated test suites that validate safety-critical systems across SIL, PIL, and HIL environments
  • Build test infrastructure that scales across hundreds of hardware configurations while maintaining rapid feedback cycles
  • Collaborate with Firmware Developers, Integration Engineers, and hardware teams to define testability requirements early in the design process
  • Create and maintain regression suites that ensure no regressions for each vehicle software delivery
  • Develop Python-based test frameworks, result analysis pipelines, and automated failure triage tools
  • Design and assist in building FPGA-based test fixtures that emulate real vehicle electrical environments
  • Drive test coverage strategies that map requirements to evidence across all validation layers


  • What You'll Bring

  • Degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience and evidence of exceptional ability
  • Fluency in Python with experience writing self-documenting, reusable, and maintainable test automation code
  • Ability to design comprehensive test cases from specifications that go beyond happy path to edge cases and failure modes
  • Strong understanding of embedded systems, communication protocols, and real-time constraints
  • Ability to read and interpret electrical schematics and use lab equipment (oscilloscopes, power supplies, electronic loads, multimeters)
  • Experience with hardware-in-the-loop systems, FPGA-based test platforms, or processor-in-the-loop testing preferred
  • Ability to collaborate on and communicate complex technical concepts across multidisciplinary teams
  • Bias toward automation, scalable infrastructure, and systematic problem-solving


  • Benefits
    Compensation and Benefits
    Along with competitive pay, as a full-time Tesla employee, you are eligible for the following benefits at day 1 of hire:
  • Medical plans > plan options with $0 payroll deduction
  • Family-building, fertility, adoption and surrogacy benefits
  • Dental (including orthodontic coverage) and vision plans, both have options with a $0 paycheck contribution
  • Company Paid (Health Savings Accounts) HSA Contribution when enrolled in the High-Deductible medical plan with HSA
  • Healthcare and Dependent Care Flexible Spending Accounts (FSA)
  • 401(k) with employer match, Employee Stock Purchase Plans, and other financial benefits
  • Company paid Basic Life, AD&D
  • Short-term and long-term disability insurance (90 day waiting period)
  • Employee Assistance Program
  • Sick and Vacation time (Flex time for salary positions, Accrued hours for Hourly positions), and Paid Holidays
  • Back-up childcare and parenting support resources
  • Voluntary benefits to include: critical illness, hospital indemnity, accident insurance, theft & legal services, and pet insurance
  • Weight Loss and Tobacco Cessation Programs
  • Tesla Babies program
  • Commuter benefits
  • Employee discounts and perks program


  • Expected Compensation

    $140,000 - $360,000/annual salary + cash and stock awards + benefits

    Pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. The total compensation package for this position may also include other elements dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.

    , Tesla

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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

    3.8

    10条评价

    工作生活平衡

    2.2

    薪酬

    3.8

    企业文化

    3.5

    职业发展

    4.1

    管理层

    2.8

    65%

    推荐给朋友

    优点

    Innovative projects and cutting-edge technology

    Great team and supportive colleagues

    Opportunities for growth and learning

    缺点

    Long hours and poor work-life balance

    High pressure and tight deadlines

    Management issues and high expectations

    薪资范围

    1,398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

    面试经验

    4次面试

    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