Fundamental AI Research Scientist – Multimodal Audio – Speech – Sound and Music – Fair

Fundamental AI Research Scientist – Multimodal Audio – Speech – Sound and Music – Fair

Requirements

• Bachelor’s degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
• PhD degree in AI, computer science, data science, or related technical fields, or equivalent practical experience.
• 2+ years of experience holding an industry, faculty, academic, or government researcher position.
• Research publications reflecting experience in related research fields: audio (speech, sound, or music) generation, text-to-speech (TTS) synthesis, text-to-music generation, text-to-sound generation, speech recognition, speech / audio representation learning, vision perception, image / video generation, video-to-audio generation, audio-visual learning, audio language models, lip sync, lip movement generation / correction, lip reading, etc.
• Familiarity with one or more deep learning frameworks (e.g. pytorch, tensorflow, …)
• Experienced in Python programming language.
• Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment.

Responsibilities

• Develop algorithms based on state-of-the-art machine learning and neural network methodologies.
• Perform research to advance the science and technology of intelligent machines.
• Conduct research that enables learning the semantics of data across multiple modalities (audio, speech, images, video, text, and other modalities).
• Work towards long-term ambitious research goals, while identifying intermediate milestones.
• Design and implement models and algorithms.
• Work with large datasets, train / tune / scale the models, create benchmarks to evaluate the performance, open source and publish.

Preferred Qualifications

• First-authored publications at peer-reviewed conferences, such as ICML, NeuRIPS, ICLR, ICASSP, Interspeech, ACL, EMNLP, CVPR, and other similar venues.
• Research and engineering experience demonstrated via publications, grants, fellowships, patents, internships, work experience, open source code, and / or coding competitions.
• Experience solving complex problems and comparing alternative solutions, trade-offs, and diverse points of view.
• Experience working and communicating cross functionally in a team environment.
• Experience communicating research findings to public audiences of peers.