The role
From Google's own posting.
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.
Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.
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: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google .
Develop audio-first models capable of orchestrating and planning complex dialogs, including leveraging external tools like search when necessary.
Leverage new sources of data (real and synthetic) to empower new real-time dialog capabilities.
Work with infra teams to design models suitable for open-mic conversations that are fluid and have low latency.
Create comprehensive datasets and benchmarks to improve and measure audio-to-audio (A2A) model performance on conversationality.
Minimum qualifications:
PhD degree in Computer Science, Electrical Engineering, or Computer Engineering, a related field, or equivalent practical experience.
Experience in artificial intelligence or machine learning.
Experience with large language models, NLP, or Generative AI.
Experience in an applied research setting.
Experience working with machine learning (ML) models, training methodologies, or evaluation datasets.
Preferred qualifications:
Experience with Generative Audio Modeling, Multimodal modeling, On-device AI Implementation or Fine tuning for LLMs.
Experience in NLP, LLMs or Generative AI.
A strong publication record in conferences or journals in the field of communications or signal processing.
PhD degree in Computer Science, Electrical Engineering, or Computer Engineering, a related field, or equivalent practical experience.
Experience in artificial intelligence or machine learning.
Experience with large language models, NLP, or Generative AI.
Experience in an applied research setting.
Experience working with machine learning (ML) models, training methodologies, or evaluation datasets.