2026 Summer Intern, MS/PhD, Software Engineer, Planner Reasoning ML/DL
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La posizione
Frame open-ended real-world problems into well-defined ML challenges for planning and reasoning under uncertainty.
Apply deep learning, reinforcement learning, and imitation learning to build robust planning solutions for complex driving scenarios.
Develop methods to learn from unsupervised driving data, exploring state-of-the-art fine-tuning techniques such as RL, LoRA, and contrastive learning.
Build data tooling and infrastructure to accelerate experimentation and evaluation.
Use data-driven metrics to compare approaches and guide development; collaborate with perception, simulation, and evaluation teams.
Ideal candidates are pursuing a Masters/PhD with strong Python and DL skills, experience with large-scale data, and interest in autonomous driving systems; experience with C++, PyTorch/JAX/TF is a plus.
Apply deep learning, reinforcement learning, and imitation learning to build robust planning solutions for complex driving scenarios.
Develop methods to learn from unsupervised driving data, exploring state-of-the-art fine-tuning techniques such as RL, LoRA, and contrastive learning.
Build data tooling and infrastructure to accelerate experimentation and evaluation.
Use data-driven metrics to compare approaches and guide development; collaborate with perception, simulation, and evaluation teams.
Ideal candidates are pursuing a Masters/PhD with strong Python and DL skills, experience with large-scale data, and interest in autonomous driving systems; experience with C++, PyTorch/JAX/TF is a plus.
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