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Tesla
Tesla

Accelerating the world's transition to sustainable energy.

CAE Engineer, Optimus

职能工程
级别中级
地点Palo Alto, California, United States
方式现场办公
类型全职
发布今天
立即申请
What To Expect
The Tesla Robotics Team designs and builds humanoid bi-pedal robots (Optimus) to perform tasks that are boring, repetitive, and dangerous for humans. As a CAE Engineer, you will guide the design of sensors and structural components using high-fidelity FE models grounded in sound engineering principles Working closely with mechanical, electrical, controls, software, and manufacturing teams, you’ll analyze and optimize designs, challenge traditional CAE methods, and pioneer more efficient, accurate approaches. This role places you at the forefront of critical design and engineering decisions, shaping a robust and reliable humanoid robot that has never been executed before.

What You'll Do

  • Develop and analyze comprehensive Finite Element (FE) models for Optimus, from individual components to the full system
  • Conduct static, dynamic, structural and MBD simulations to evaluate and validate the performance against key requirements for strength, stiffness, durability and sensing
  • Build and refine simulation workflows to assess performance across diverse operating scenarios, automate processes, documentation, and tools to improve efficiency, accuracy, and repeatability
  • Provide design guidance to world-class engineering teams, enabling data-driven product decisions
  • Collaborate with Test Engineering to design and validate experiments, correlate test data with simulations, and enhance predictive modeling capabilities
  • Establish and maintain modeling standards, methods, and best practices for current and future programs
  • Material characterization and advanced modeling of structural and sensing components
  • Plan and execute large-scale DOEs, creating ML driven surrogate models to extract insights to drive key design decisions


  • What You'll Bring

  • Degree in Mechanical Engineering or a related field, or equivalent experience
  • Strong foundation in Engineering Mechanics, Computational Mechanics, Material Science, and Finite Element Theory and applications
  • Demonstrated ability to guide structural, sensing, thermal, and mechanism designs through CAE, from concept to mass production, across robotics, automotive, and consumer electronics
  • Expertise in building high-fidelity FE models at component, sub-system, and full-system levels
  • Proficiency in linear and non-linear static, dynamic and modal simulations and experience developing multi-physics simulation workflows (e.g., thermal-mechanical, mechanical-electrical)
  • Advanced knowledge in material characterization and modeling, including nonlinear elasticity, viscoelasticity, creep, and strain-rate dependent plasticity in Metals, Plastics, Polymers and Adhesives
  • Proficiency in CAE Tools such as BETA CAE, LS-DYNA, Abaqus, ADAMS, COMSOL, OPTISTRUCT, Design Life, FE Safe
  • Strong programming and data analysis skills in MATLAB, Python, or equivalent
  • Excellent communication and interpersonal skills with the ability to collaborate effectively across cross-functional teams


  • 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

    $108,000 - $264,000/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.

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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