info_outline
X Applicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.Note: By applying to this position you will have an opportunity to share your preferred working location from the following:
Mountain View, CA, USA; San Francisco, CA, USA.
Minimum qualifications: - PhD in Computer Science, a related field, or equivalent practical experience.
- 3 years of experience in development with JAX, PyTorch, or TensorFlow.
- 3 years of experience with machine learning and machine learning algorithms.
- 3 years of experience with Generative Artificial Intelligence (GenAI) techniques (e.g., Large Language Models, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).
- 2 years of experience leading a research agenda.
- Experience in academic research within machine learning, publications, or research in related fields.
Preferred qualifications: - 2 years of coding experience.
- 1 year of experience leading research efforts and influencing other researchers.
About the jobWe research and develop machine learning models for billions of Google users.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $252000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Drive foundational research in next-generation generative modeling (e.g., autoregressive architectures, diffusion models) to pioneer breakthroughs across multimodal generation, including image, video, and audio synthesis.
- Formulate novel scientific methodologies and conduct deep literature reviews to solve complex, open-ended AI challenges, exercising independent judgment to balance immediate project milestones with long-term frontier research.
- Pioneer model optimization and efficient inference strategies to significantly reduce compute overhead, optimize latency, and scale large multimodal systems across high-performance infrastructure.
- Advance post-training and capability scaling using reinforcement learning to enhance model alignment, reasoning, and multi-turn generation quality across modalities.
- Apply rigorous engineering and experimental practices to design robust benchmarks, measure real-world performance, and systematically validate the scientific and practical impact of research findings.
