• Design, implement, and train state-of-the-art neural network architectures for robotic applications, including large multimodal models (vision, language, sensor data) and generative models.
• Architect and maintain scalable deep learning frameworks for training on massive, real-world robot datasets.
• Develop and implement cutting-edge techniques in areas such as imitation learning, reinforcement learning, self-supervised learning, and video-based models.
• Own the full machine learning lifecycle: define data collection and labeling strategies, build high-throughput data pipelines, and establish robust evaluation and deployment protocols.
• Collaborate closely with software, hardware, and controls teams to integrate and test your models on physical robots.
• Leverage and contribute to internal tooling for large-scale data processing, model experimentation, and continuous integration.

Requirements:

• Proven experience (M.S., Ph.D., or equivalent industry experience) in Computer Science, AI, or a related field.
• Strong software engineering skills are non-negotiable; demonstrated excellence in solving complex engineering problems, particularly in Python.
• A deep, "under the hood" understanding of modern deep learning, including network architectures, loss functions, optimization, and transformers.
• Extensive hands-on experience with PyTorch.
• A proven track record of training large-scale deep learning models on large datasets.
• Ability to thrive in a fast-paced, high-impact environment where solutions are often unclear and require deep exploration.

Advantages:

• Experience with training large models (e.g., LLM, VLM, VLA).
• Prior experience working with robotic learning systems.
• Experience with multi-task learning, imitation learning, or reinforcement learning.
• A strong publication record in top-tier AI or robotics conferences (e.g., NeurIPS, ICML, CVPR, RSS, CoRL).