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AI Engineer
Policy & Video Generation, Tesla AI
Palo Alto, CA
What To Expect
Recent research has shown that it is possible to post-train video generation models into video action models that work well as generalized policies. At Tesla, we're pushing the boundaries of video action models with the goal of deploying these models that run at real-time on the car and bot. We are also interested in expanding the capabilities of these models to omni models that are able to take multimodal inputs (e.g. video, audio, text, proprioception) and generate multimodal outputs (e.g. video, audio, text, actions). You will have the opportunity to work on the full stack of model training and deployment, including data, pre-training, post-training, reinforcement learning and distillation.
What You'll Do
- Design and train video action models that predict future frames and actions
- Expand input and output modalities of video generation models into multimodal omni models
- Explore different architectures for multimodal generation
- Apply reinforcement learning and distillation to video action models with the goal of real-time inference
- Experiment with data and training recipes that optimize for policy performance and generalization
- Optimize distributed pipelines for large-scale multimodal training
- Collaborate across Autonomy and Robotics to align model design, evaluation, and deployment
What You'll Bring
- Expertise in generative video or robotics policy architectures
- Strong background in spatiotemporal modeling, 3D scene understanding, or neural simulation
- Proficiency with PyTorch or JAX
- Experience in large-scale distributed training, especially the different forms of parallelism
- Familiarity with reinforcement or imitation learning in simulated or embodied environments
- Curiosity about building intelligent systems that understand and generate the world around them
Expected Compensation: $124,000 - $420,000/annual salary + cash and stock awards + benefits
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