Research Scientist, Audio Language Models

Key Skills

Flax Programming Language, Google Products, Infrastructure as code, Java Programming, Large scale data processing, Machine learning techniques, Python Programming, SQL

Job Description

Minimum qualifications:

• Experience in machine learning algorithms
• Experience in machine learning infrastructure

Preferred qualifications:

• PhD in Computer Science, a related technical field, or equivalent practical experience
• Experience in machine learning, speech, and general audio processing
• Experience programming in Python

About the job

At Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.

Our team is focused on Machine Learning based models for audio processing, with an emphasis on models that generate audio as output including, for example, speech enhancement, bandwidth extension, neural audio compression, audio in-filling, and more. We have ongoing collaborations with several other teams in Research as well as with product teams, including Google Meet, Pixel, and YouTube. This allows us to advance scientific publications and at the same time push the outcome of our research projects to Google products.

Google Research addresses challenges that define the technology of today and tomorrow. From conducting fundamental research to influencing product development, our research teams have the opportunity to impact technology used by billions of people every day.

Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field -- we publish regularly in academic journals, release projects as open source, and apply research to Google products.

Responsibilities

• Propose, implement, and evaluate novel, deep-learning-based approaches operating on discrete audio and text representations, exploring use cases that can be applied in Google products.
• Contribute to the development of training/evaluation infrastructure to scale in terms of data and model complexity.
• Publish in audio and machine learning conferences.