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Internship, Cybersecurity Network Applications Engineer, Energy Engineering (Summer 2026)

Tesla

Internship, Cybersecurity Network Applications Engineer, Energy Engineering (Summer 2026)

Tesla

Palo Alto, California

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On-site

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Internship

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Today

What To Expect
Consider before submitting an application:

This position is expected to start May 2026 and continue through summer term (ending approximately August 2026 or later, if available). We ask for a minimum of 12 weeks, full-time (40 hours/week) and on-site, for most internships. Our internship program is for students who are actively enrolled in an academic program. Recent graduates seeking employment after graduation and not returning to school should apply for full-time positions, not internships.

International Students: If your work authorization is through CPT, please consult your school on your ability to work 40 hours per week before applying. You must be able to work 40 hours per week on-site. Many students will be limited to part-time during the academic year.

The Internship Recruiting Team is driven by the passion to recognize and develop emerging talent. Our year-round program places the best students in positions where they will grow technically, professionally, and personally through their experience working closely with their Manager, Mentor, and team. We are dedicated to providing an experience that allows the intern to experience life at Tesla by including them in projects that are critical to their team’s success.

About The Team
Join our team as an Application Engineering Intern, where you will play a pivotal role in the developing cybersecurity and networking applications for Megapack, Powerpack, and Industrial Batteries. In this dynamic position, you'll lead a variety of cross-functional projects, focusing on research, proof of concept, design, testing and validation of network and cybersecurity features. This internship is a unique opportunity to collaborate with experienced professionals who are pioneering technologies that will transform the energy landscape. If you are eager to take on challenges, learn from the best, and drive innovation, we invite you to join our dynamic team and be part of the revolution in the energy industry.

Location: Palo Alto, CA

What You'll Do

  • Enhancing testing capabilities for network and cybersecurity features by building new test beds or expanding existing infrastructure
  • Support gathering information for cybersecurity compliance initiatives and audits
  • Evaluate design options and product configurations via lab/onsite testing
  • Develop scripts to automate recurring test scenarios


  • What You'll Bring

  • Pursuing a degree in Computer, Electrical, or related field
  • Familiar with cybersecurity of industrial control systems and relevant Energy industry standards such as IEC 62443, and NERC-CIP
  • Familiar with Energy systems including devices such as meters, inverters, SCADA controllers, and network systems
  • Hands-on experience in lab testing network & cybersecurity features is desired
  • Excellent analytical and problem-solving skills
  • Self-motivated and able to work independently as well as part of a team
  • Skilled with automation and programming languages like Python, C, C++, RUST, GO etc.


  • Benefits
    Compensation and Benefits
    As a full-time Tesla Intern, you will be eligible for:
  • Medical plans > plan options with $0 payroll deduction
  • Family-building, fertility, adoption and surrogacy benefits
  • Dental (including orthodontic coverage) and vision plans. Both have an option with a $0 payroll contribution
  • Company Paid (Health Savings Account) HSA Contribution when enrolled in the High Deductible Medical Plan with HSA
  • Healthcare and Dependent Care Flexible Spending Accounts (FSA)
  • 401(k), Employee Stock Purchase Plans, and other financial benefits
  • Company Paid Basic Life, AD&D, and short-term disability insurance (90 day waiting period)
  • Employee Assistance Program
  • Sick and Vacation time (Flex time for salary 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
  • Commuter benefits
  • Employee discounts and perks program


  • Expected Compensation

    $20.00 - $50.00/hour + 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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    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