About the job
At Rhoda AI, we are pioneering the development of a comprehensive platform for the next generation of humanoid robots. Our ambition encompasses everything from high-performance, software-defined hardware to the foundational models and video world models that govern their operations. Our robots are engineered as versatile generalists, adept at navigating intricate, real-world environments and addressing scenarios that are not encountered during training. We operate at the confluence of large-scale learning, robotics, and systems, with a research team that includes esteemed researchers from Stanford, Berkeley, Harvard, and other renowned institutions. Rather than merely enhancing a feature, we are constructing an entirely new computing platform dedicated to physical tasks. With over $400M raised, we are aggressively investing in research and development, hardware innovation, and scaling up manufacturing to realize this vision.
Key Responsibilities
Lead research initiatives focused on foundational models and world models for robotics, including representation learning, dynamics/prediction, planning, and control.
Define research challenges and formulate hypotheses rooted in real-world robotic autonomy requirements.
Design and execute rigorous experiments at scale, encompassing ablations, benchmarking, and evaluation methodologies.
Develop and assess model architectures aimed at enhancing long-horizon predictions, rollout quality, and overall robotic task performance.
Investigate and improve pre-training and post-training processes, including fine-tuning, alignment, and evaluation of large multimodal models.
Collaborate closely with Research Engineers to translate innovative ideas into scalable training pipelines and dependable systems.
Effectively communicate research findings through internal documentation, presentations, and reviews.
Publish and present research at prestigious venues.
Required Qualifications
Ph. D. in a relevant discipline such as Machine Learning, Robotics, Computer Science, Electrical Engineering, Applied Mathematics, or Computer Vision.
Demonstrated strong publication record in high-quality research (e.g., NeurIPS, ICML, ICLR, CoRL, RSS, ICRA, CVPR).
In-depth knowledge of current machine learning techniques, particularly in areas such as:
Deep learning and representation learning.
Sequence modeling and transformers.
Generative modeling (e.g., diffusion, autoregressive, latent-variable models).
