Staff Research Scientist, Speech, Language, Machine Learning

Minimum qualifications: PhD degree in Computer Science, a related field, or equivalent practical experience. 4 years of experience with research agendas across multiple teams or projects. Experience in speech research, acoustic modeling, and automatic speech recognition (ASR). One or more scientific publication submission(s) for conferences, journals, or public repositories. Preferred qualifications: 2 years of experience in coding and leading multiple research efforts and influencing research direction. Experience with multilingual, low resource speech recognition. Experience with components of a speech recognition system including acoustic, language modeling and decoder search. Experience with Text to speech (TTS) and bringing latest Machine Learning (ML) methods to expressive synthesis. Excellent theoretical skills in statistics, signal processing and applications to speech recognition. About the job: 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 set up 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. In this role, you will focus on languages with vast amounts of data as well as those with limited data availability such as Indian, African and other languages in the NBU market. To address languages in the NBU markets you will explore techniques including data augmentation, incorporating external knowledge via Large Language Models (LLM), and Neural Network (NN) architectures and objectives that can bridge the quality gap when compared with tier one languages where data availability is not a problem. You will bring multiple modalities (speech, text and image) and enhancement/robustness techniques to one model. The US base salary range for this full-time position is $189,000-$284,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google. Responsibilities: Generate the impact of research results across organizations and in the research community by authoring research papers. Assist in research growth by sharing research trends and best practices within the community by reviewing academic papers, and serving on program committees and grant panels. Deliver on large portions of a project by defining the data structure, framework, design, and evaluation metrics for research solution development and implementation. Identify timelines and obtain resources needed. Identify new and upcoming research areas by interacting with potential external and internal collaborators. Develop long-term research strategy and plans to expand the impact of Google research. Identify complex but defined problems or gaps in existing technology and engage stakeholders and leaders to address them.

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