Senior Research Scientist, Foundational Audio

• Define and lead research agendas, experimental designs, and evaluation benchmarks in sequence, speech, audio, and multimodal modeling.
• Co-design and optimize models for scaling laws, memory or cache efficiency, distillation, and low-latency streaming inference in JAX, PyTorch, or TensorFlow.
• Contribute to open-source toolkits, and mentor researchers.

Minimum qualifications:

• PhD in Computer Science, Linguistics, Electrical Engineering, Mathematics, Physics, a related field, or equivalent practical experience.
• 2 years of experience leading a research agenda in sequence, speech, or audio modeling.
• Experience with sequence-to-sequence models across state-space or linear attention, token-free latent representations, speech-text modeling, diffusion, flow-matching, speculative decoding, or streaming alignment.
• Experience with hardware-aware model co-design, scaling laws, memory or cache optimization, or streaming inference latency in JAX, PyTorch, or TensorFlow.
• One or more peer-reviewed publications in machine learning, speech, or audio conferences or journals.

Preferred qualifications:

• 10 years of experience in the theoretical and algorithmic aspects of sequence modeling and machine learning research.
• 4 years of experience setting research agendas across multiple projects or teams, and publications in NeurIPS, ICML, ICLR, JMLR, IEEE Transactions, Nature, Interspeech, ICASSP, or ACL.
• Experience building, scaling, pre-training, and post-training foundational audio, speech, or multimodal models using reinforcement learning alignment or multi-resolution modeling.
• Experience with model compression, distillation, or machine learning acceleration on TPUs or GPUs, and establishing evaluation benchmarks.
• Contributions to open-source machine learning or speech toolkits such as JAX, PyTorch, Hugging Face Transformers, Kaldi, K2, or ESPNet.

• PhD in Computer Science, Linguistics, Electrical Engineering, Mathematics, Physics, a related field, or equivalent practical experience.
• 2 years of experience leading a research agenda in sequence, speech, or audio modeling.
• Experience with sequence-to-sequence models across state-space or linear attention, token-free latent representations, speech-text modeling, diffusion, flow-matching, speculative decoding, or streaming alignment.
• Experience with hardware-aware model co-design, scaling laws, memory or cache optimization, or streaming inference latency in JAX, PyTorch, or TensorFlow.
• One or more peer-reviewed publications in machine learning, speech, or audio conferences or journals.