Members of the team work on research and develop audio representations that best captures both the semantic and acoustic information within the audio signal to enable Gemini models to understand audio inputs and also generates natural audio sounds. This builds the foundation for audio-to-audio dialog models, text-to-speech, speech-to-speech translation, duplex audio dialogs.
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We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $253000 (USD) + 15% bonus target + equity + benefits
Learn more about
benefits at Google.
Responsibilities
- Design, build, and maintain training infrastructure to support Gemini Audio encoder and pretraining models, focusing on Accelerated Linear Algebra (XLA) optimization for training on Tensor Processing Units (TPUs).
- Implement tools to analyze and track audio model training efficiency and health.
- Contribute and work closely with Gemini to build and maintain audio related components.
- Collaborate with research teams across Gemini Audio to improve the model training and inference efficiency.
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with software development in Python.
- 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
- Experience with compiler optimization, code generation, and runtime systems for popular accelerators, including GPU or TPU.
- Experience with Machine Learning, Machine Learning Optimization, Performance Optimization, and Large Language Models.
Preferred qualifications:
- Master's degree or PhD in Computer Science or related technical field.
- Experience with TPU architectures, memory hierarchies, performance bottlenecks, and Accelerated Linear Algebra.
- Experience tailoring algorithms and ML models to exploit TPU architecture strengths and minimize weaknesses.
- Experience with various stack layers: compilers (OpenXLA, MLIR), serving libraries/frameworks (vLLM, sglang) and ML frameworks (JAX, PyTorch).
- Debugging experience to improve performance of single-mode or multi-mode (distributed) systems.
Note: By applying to this position you will have an opportunity to share your preferred working location from the following:
Mountain View, CA, USA; New York, NY, USA.