Applied Sciences INTERN

Improve and devise new methodologies for evaluation of audio/text collaborative agents.

Improve and devise better RL recipes for training/fine-tuning collaborative models including synthetic data generation, and the development and calibration of graders.

Conduct experiments, create and validate metrics, and develop candidate algorithms to improve the existing capabilities of current models and agent implementations in any of the above topics.

Collaborate closely with CMD Labs researchers and engineers to leverage existing assets, datasets, and ensure results can contribute to the product.

Embody our culture and values.

Required Currently enrolled in a PhD program (or published candidate in MSc program) in Computer Science, Electrical or Computer Engineering, Statistics, or a related field.

Practical experience in training, fine-tuning, and prompt engineering of transformer models or LLMs.

Practical Python coding experience leveraging modern coding agents.

Field of research and publications directly related to Audio LLMs or collaborative models and agents.