Senior Research Scientist, Machine Learning Efficiency

Job Title: Senior Research Scientist, Machine Learning Efficiency

Company: Google

Location: Multiple locations available

Minimum Qualifications:

• PhD in Computer Science, a related technical field, or equivalent practical experience.
• 7+ years of experience in Machine Learning (ML), ML Efficiency, ML Optimization, or a related field.
• Proven track record in contributing to the research community, including publications in prestigious forums such as ICML, ICLR, NeurIPS.
• Proficiency in programming languages such as Python or C/C++.

Preferred Qualifications:

• Demonstrated experience in pioneering research and innovation.
• Strong coding skills and experience in software development.
• Experience collaborating effectively within a research team environment.
• Excellent problem-solving skills with the ability to navigate through ambiguity.

About the Role:

As a Senior Research Scientist at Google, you will be part of a dynamic team that pushes the boundary of machine learning and its applications. You will be responsible for designing and executing large-scale experiments, quickly deploying promising ideas, and contributing to both theoretical and practical advancements in computer science. Our projects cover a vast range of topics from machine learning, data mining, natural language processing, to software and hardware performance optimization, and core search technologies.

Your work will influence the next generations of technology, improving the efficiency of machine learning models and the speed at which they operate. You will also have the honor of contributing back to the global research community by publishing your findings and collaborating with academic institutions worldwide.

Key Responsibilities:

• Advance fundamental algorithms and model architectures, enhancing the efficiency of training deep learning systems and their generalization.
• Develop innovative solutions for efficient inference in foundational models including techniques like knowledge adoption and distillation.
• Optimize data subset selection and training methodologies for handling extensive datasets.
• Enhance the model deployment pipeline across various stages including pretraining, fine-tuning, and reinforcement learning from human feedback (RLHF).

This is a remarkable opportunity to lead groundbreaking research in a vibrant and conducive environment at Google, enabling the realization of next-generation intelligent systems. If you are driven by innovation and a desire to shape the future, we would love to hear from you.

How to Apply:

To submit your application, please update your latest resume and list of publications, and apply through our careers page. We are excited to review your contributions and explore how you can make a significant impact at Google Research.

Google is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.