About the job
At Rhoda AI, we are pioneering the development of a comprehensive foundation for the next generation of humanoid robots. Our approach integrates high-performance, software-defined hardware with advanced models and world models that facilitate their operation. Our robots are engineered to function as generalists, adept at navigating complex, real-world environments and managing previously unseen scenarios. We are at the forefront of large-scale learning, robotics, and systems research, supported by a diverse team of experts from prestigious institutions including Stanford, Berkeley, and Harvard. With over $400 million raised, we are committed to investing in the R&D, hardware innovation, and manufacturing scale-up necessary to bring our vision to life.
We invite applications for the position of Research Engineer, where you will collaborate closely with our research team on comprehensive model development. This hands-on role encompasses the entire stack: data management, infrastructure, model training, and deployment. You will play a critical role in transforming research concepts into scalable, operational systems, including the learning and application of world models for planning, prediction, and control.
Key Responsibilities
Design and develop foundational and world models for extensive robotic learning.
Establish and manage data pipelines encompassing collection, curation, filtering, and augmentation for multimodal robotic data (vision, proprioception, actions, language, video).
Engage in pre-training and post-training processes, including fine-tuning, alignment, and evaluation of large models and world models.
Implement and experiment with various model architectures.
Create training and evaluation frameworks for world models, focusing on rollout quality, long-horizon predictions, and downstream task performance.
Enhance training infrastructure and workflows (distributed training, efficiency, debugging).
Collaborate closely with researchers to convert ideas into resilient, scalable implementations.
Assist with experiments, ablations, and real-world deployments on robotic systems.
Qualifications
Proficiency in software engineering combined with a research-driven mindset.
Demonstrated experience in implementing ML models end-to-end, beyond merely executing existing code.
Comprehensive understanding of the entire ML pipeline: data → pre-training → post-training → evaluation → deployment.
Strong foundation in deep learning frameworks and methodologies.
Ability to work collaboratively in a fast-paced, innovative environment.
