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职位Tesla

AI Engineering Intern, Summer 2026

Tesla

AI Engineering Intern, Summer 2026

Tesla

Palo Alto, California

·

On-site

·

Internship

·

Today

必备技能

Python

PyTorch

TensorFlow

Machine Learning

What To Expect
Tesla is seeking exceptional Machine Learning Interns to help build large scale models to drive the future of autonomy across all current and future generations of Tesla AI products. You will work on a lean team without boundaries and have access to one of the world’s largest training clusters with a data engine that constantly generates new information for improving our models. Most importantly, you will see your work repeatedly shipped to and utilized by millions of Tesla’s customers.

We are seeking Interns in the following AI disciplines:
  • Train large-scale foundation and generative models that are optimized for performance and latency
  • Improve data engine for large scale and high-quality dataset curation
  • Reinforcement Learning for instilling objectives and improving overall robustness, including RL for training
  • Design compound AI systems for better planning and reasoning, with emphasis on world modeling and generative videos


  • What You'll Do

  • Applied research in the areas of Foundation Models, including but not limited to computer vision, large language models and generative modeling
  • Work on cutting-edge techniques in AI - multi-task learning, video networks, multi-modal generative models, imitation learning, reinforcement learning, semi-supervised learning, self-supervised learning
  • Explore and implement novel AI tooling and techniques for efficient training and fine-tuning of large-scale models, incorporating RL for training
  • Leverage millions of miles of driving data and interventions to build a robust and scalable end-to-end learning based self-driving system
  • Collaborate with a team to apply research findings to real-world challenges, ensuring high-quality system integration within existing platforms
  • Experiment with data generation and network driven data collection approaches to enhance the diversity and quality of training data, including generative videos
  • Ship production quality, safety-critical software to the entirety of Tesla’s vehicle fleet, with applications in world modeling


  • What You'll Bring

  • Demonstrated experience in machine learning frameworks and models such as PyTorch, TensorFlow, GPT, CNNs, and generative models
  • Strong experience with Python and software engineering best practices
  • Experience with one or more of imitation Learning, reinforcement learning (offline/off-policy), modern neural network architectures (e.g., GPT, diffusion, generative models), or related techniques, including RL for training
  • An “under the hood” knowledge of deep learning: layer details, loss functions, optimization, etc.
  • Prior experience with sparse training techniques, neural network pruning, and generative modeling, with exposure to generative videos
  • Experience with training large models on distributed computing
  • Ability to work on complex problems and produce significant research and/or experience deploying production ML models at scale, particularly in world modeling
  • Proven track record of innovations and executions in deep learning, demonstrated with shipping products or first-author publications at leading AI conferences


  • 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

    $100,000 - $150,000/annual salary + 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

    Senior/L5

    Junior/L3 · Data Annotation Specialist

    373份报告

    $49,465

    年薪总额

    基本工资

    $49,465

    股票

    -

    奖金

    -

    $35,995

    $67,975

    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