Research Scientist – Speech and Audio Understanding (Large Models & Multimodal Systems)

Job Responsibilities:
We are building large-scale, native multimodal model systems that jointly support vision, audio, and text to enable comprehensive perception and understanding of the physical world. You will join the core research team focused on speech and audio, contributing to the following key research areas:
Develop general-purpose, end-to-end large speech models covering multilingual automatic speech recognition (ASR), speech translation, speech synthesis, paralinguistic understanding, and general audio understanding.
Advance research on speech representation learning and encoder/decoder architectures to build unified acoustic representations for multi-task and multimodal applications.
Explore representation alignment and fusion mechanisms between audio/speech and other modalities in large multimodal models, enabling joint modeling with image and text.
Build and maintain high-quality multimodal speech datasets, including automatic annotation and data synthesis technologies.
Work Location: US-Washington-Bellevue

Ph.D. in Computer Science, Electrical Engineering, Artificial Intelligence, Linguistics, or a related field; or Master’s degree with several years of relevant experience.
Solid understanding of speech and audio signal processing, acoustic modeling, language modeling, and large model architectures.
Proficient in one or more core speech system development pipelines such as ASR, TTS, or speech translation; experience with multilingual, multitask, or end-to-end systems is a plus.
Candidates with in-depth research or practical experience in the following areas are strongly preferred:
Speech representation pretraining (e.g., HuBERT, Wav2Vec, Whisper)
Multimodal alignment and cross-modal modeling (e.g., audio-visual-text)
Experience driving state-of-the-art (SOTA) performance on audio understanding tasks with large models
Proficient in deep learning frameworks such as PyTorch or TensorFlow; experience with large-scale training and distributed systems is a plus.
Familiar with Transformer-based architectures and their applications in speech and multimodal training/inference.。加分项：