トレンド企業

Tesla
Tesla

Accelerating the world's transition to sustainable energy.

CAE Engineer, Optimus

職種エンジニアリング
経験ミドル級
勤務地Palo Alto, California, United States
勤務オンサイト
雇用正社員
掲載今日
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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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    Teslaについて

    Tesla

    Tesla

    Public

    A financial leasing taxi company that provides vehicles to customers

    140,000+

    従業員数

    Ciudad De Panamá

    本社所在地

    $800B

    企業価値

    レビュー

    10件のレビュー

    3.8

    10件のレビュー

    ワークライフバランス

    2.3

    報酬

    4.0

    企業文化

    3.2

    キャリア

    4.1

    経営陣

    2.8

    65%

    知人への推奨率

    良い点

    Innovative projects and cutting-edge technology

    Great team and supportive colleagues

    Good compensation and benefits

    改善点

    Long hours and poor work-life balance

    High pressure and tight deadlines

    Management issues and high expectations

    給与レンジ

    1,403件のデータ

    Junior/L3

    Mid/L4

    Junior/L3 · Associate Analyst

    2件のレポート

    $94,875

    年収総額

    基本給

    $82,500

    ストック

    -

    ボーナス

    -

    $92,000

    $97,750

    面接レビュー

    レビュー4件

    難易度

    3.5

    / 5

    期間

    14-28週間

    体験

    ポジティブ 0%

    普通 75%

    ネガティブ 25%

    面接プロセス

    1

    Application Review

    2

    Recruiter Screen

    3

    Technical Phone Screen

    4

    Take-home Assignment

    5

    Panel Interview

    6

    Offer

    よくある質問

    Coding/Algorithm

    Technical Knowledge

    Behavioral/STAR

    System Design

    Machine Learning Concepts