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Manufacturing Engineer, Tabless (m/w/d) – Gigafactory Berlin-Brandenburg

Tesla

Manufacturing Engineer, Tabless (m/w/d) – Gigafactory Berlin-Brandenburg

Tesla

Grünheide (mark), Brandenburg

·

On-site

·

Full-time

·

Today

Required Skills

Project Management

What To Expect
Tesla is accelerating the world's transition to sustainable energy. Revolutionary strategies and products were developed within a few years and successfully launched on a large scale. This is only possible through extraordinary speed, innovation and efficiency. Gigafactory Berlin-Brandenburg forms the perfect basis for rolling out Tesla's incredible success story in Europe. The most important pillar for this is our employees. Their passion, motivation and engagement ensure that we achieve our goals. We are looking for you to continue and expand this success story together.

Tesla is seeking a highly motivated Manufacturing Engineer to commission, troubleshoot and ramp automation equipment. The engineer will also refine and develop processes. This role is with the Cell Manufacturing Engeering, Tabless – “Welder” team. This engineer will lead the early lifecyle of complex, fully automated, and hi-volume manufacturing equipment: from power-on to validation, and will spend a significant portion of his/her time on the production floor. The scope of equipment and processes include as laser welding, hi-speed pick and place, computer vision, insertions, press-fits, etc. Other responsibilities would include run trials/DoEs, planning projects, developing spefications, writing procedures, interfacing with vendor, driving budget approvals, and redesigning equipment. The engineer will work closely and cross-coordinate with Tesla’s internal Production Engineering, Maintenance, Quality Engineering, NPI and Project Management teams.

What You'll Do

  • Commission, validate with statistics, and ramp up automated manufacturing lines/equipment
  • Troubleshoot equipment, and setup and tune processes; root cause analyze and fix deviations
  • Perform experiments to improve existing processes, or to verify or develop new processes
  • Analyze and visualize production and trial data; present conclusions to cross-functional teams
  • Generate equipment specifications, manufacturing instructions, and other documentation
  • Collaborate with Production Engineering, Quality Engineering, and NPI teams on yield, quality and availability improvements
  • Coordinate installations and upgrades with vendors and internal suppliers


  • What You'll Bring

  • Degree in Manufacturing or Mechanical Engineering or related degree, or equivalent experience
  • Experience in a high-volume manufacturing environment, and in hands-on electromechanical troubleshooting
  • Skilled in data analysis (summary, regressions) and visualizations; experience with a data analysis software
  • Has knowledge of electrical (DC) circuits fundamentals, and able to read schematics
  • Knowledgeable in quality concepts and related statistics (SPC, capability)
  • Able to perform mechanical tolerance stack / GD&T analyses; experience with CAD software


  • , Tesla

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    About Tesla

    Tesla

    Tesla

    Public

    A financial leasing taxi company that provides vehicles to customers

    140,000+

    Employees

    Ciudad De Panamá

    Headquarters

    $800B

    Valuation

    Reviews

    3.1

    5 reviews

    Work Life Balance

    1.5

    Compensation

    1.2

    Culture

    1.3

    Career

    1.8

    Management

    1.1

    15%

    Recommend to a Friend

    Pros

    Strong financial performance

    Revenue growth

    Company achieving targets

    Cons

    Poor compensation and raises below inflation

    Union-busting and anti-labor practices

    Unpaid work demands and wage theft

    Salary Ranges

    3,570 data points

    Junior/L3

    Mid/L4

    Junior/L3 · Associate Analyst

    2 reports

    $94,875

    total / year

    Base

    $82,500

    Stock

    -

    Bonus

    -

    $92,000

    $97,750

    Interview Experience

    4 interviews

    Difficulty

    3.5

    / 5

    Duration

    14-28 weeks

    Experience

    Positive 0%

    Neutral 75%

    Negative 25%

    Interview Process

    1

    Application Review

    2

    Recruiter Screen

    3

    Technical Phone Screen

    4

    Take-home Assignment

    5

    Panel Interview

    6

    Offer

    Common Questions

    Coding/Algorithm

    Technical Knowledge

    Behavioral/STAR

    System Design

    Machine Learning Concepts