Senior Research Scientist, Foundational Audio

As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world. Google Research is building the next generation of intelligent systems for all Google products. To achieve this, we’re working on projects that utilize the latest computer science techniques developed by skilled software developers and research scientists. Google Research teams collaborate closely with other teams across Google, maintaining the flexibility and versatility required to adapt new projects and foci that meet the demands of the world's fast-paced business needs. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google . 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.