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Tesla Internship AI Engineer Self 
United States, California, Palo Alto 
552224972

23.06.2024
What to Expect

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
  • Design compound AI systems for better planning and reasoning
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
  • 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
  • Ship production quality, safety-critical software to the entirety of Tesla’s vehicle fleet
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
  • 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
  • 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
  • Proven track record of innovations and executions in deep learning, demonstrated with shipping products or first-author publications at leading AI conferences