Tesla Internship AI Engineer Self 

United States, California, Palo Alto 

552224972

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

• 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

• 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