Members of Technical Staff, Physical AI (Robotics / World Models)

Orbifold AI is advancing the frontier of physical AI and world models through rigorous evaluation and curated real-world data. They are seeking Members of Technical Staff to build data and evaluation foundations for world models and embodied AI systems, focusing on improving model quality and performance through systematic evaluation and data curation.

Responsibilities:

• Co-design data pipelines and model training workflows end to end with robotics and world model teams
• Define how multimodal data (video, image, audio, sensor, text) should be structured and indexed for training and evaluation
• Build scalable systems for data ingestion, cleaning, annotation, and taxonomy-driven curation and balanced sampling across the failure modes that matter for partner deployments
• Work on training or fine-tuning models for perception, policy learning, or multimodal reasoning including automated critics and judges that approximate human evaluation at scale
• Iterate on datasets and training setups based on downstream model performance, closing the loop between evaluation findings and the next round of data curation
• Build comprehensive evaluation frameworks, including fine-grained failure taxonomies, edge case discovery, long-tail probing, distribution shift, robustness, and behavior under adversarial or out-of-spec inputs
• Bridge gaps between raw data, dataset design, and model behavior, and translate evaluation findings into concrete data and training recommendations for partner teams