About Google
Learn more about Google. Explore our innovative AI products and services, and how we. Backed by Kleiner Perkins.
About the role
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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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
More about this role
- 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.
- 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.
- 10 years of experience in the theoretical and algorithmic aspects of sequence modeling and machine learning research.
- 4 years of...
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