Description:

The Machine Learning Engineer for Audio will focus on making advanced speech-to-text and text-to-speech technologies more accessible to the open-source community.
The role involves working with existing open-source libraries, such as Transformers, to enhance support for robust speech-to-text, speaker diarization, and text-to-speech functionalities.
The engineer will lead the creation of new open-source libraries for machine learning in audio.
The position includes fostering one of the most active machine learning communities, assisting users in contributing to and utilizing the tools developed.
Daily interactions will occur with researchers, ML practitioners, and data scientists through platforms like GitHub, Discord, forums, or Slack.

Requirements:

A passion for open-source and new technologies in text-to-speech and speech-to-text is essential.
Industry experience in speech recognition, speaker diarization, dialogue systems, or text-to-speech is considered a plus.
Candidates are encouraged to apply even if they do not meet every requirement, as the company values diverse skills, experiences, and backgrounds.

Hugging Face promotes a culture of diversity, equity, and inclusivity, ensuring all employees feel respected and supported.
Employees will have access to reimbursement for relevant conferences, training, and education to support their development.
The company offers flexible working hours and remote work options, along with health, dental, and vision benefits for employees and their dependents.
Parental leave and flexible paid time off are also provided.
Remote employees have the opportunity to visit office spaces in NYC and Paris, and the company will outfit their workstation for success.
All employees receive company equity as part of their compensation package, allowing them to benefit from the company's success.
Hugging Face supports the ML/AI community through collaboration and shared advancements in the field.