Join Google as a Staff Research Scientist in Machine Learning Efficiency

Are you ready to drive cutting-edge research and innovation in machine learning? Google Research is looking for a highly skilled Staff Research Research Scientist to advance our machine learning efficiency in projects that push the boundaries of computer science. This role offers the chance to work on ground-breaking technology that impacts all Google products.

Minimum Qualifications

• PhD in Computer Science or a closely related technical field, or equivalent practical experience.
• 4+ years of experience managing research agendas across multiple teams or projects in areas like Machine Learning (ML), ML Efficiency, or ML Optimization.
• Proficient with programming languages such as Python or C/C++.
• Experience with submitting scientific publications for conferences, journals, or public repositories (e.g., ICML, ICLR, NeurIPS).

Preferred Qualifications

• Prior experience in pioneering research initiatives.
• Demonstrated leadership within research teams.
• Strong ability to navigate and thrive in ambiguous situations.

About the Role

As a Research Scientist at Google, you will set up large-scale tests, deploy promising ideas widely, and manage both deadlines and deliverables. Your work will focus on developing new products and technologies across the spectrum of computer science, including areas like machine learning, natural language processing, and software performance analysis.

You will have the freedom to emphasize specific types facets of your work and actively contribute to the wider research community by sharing and publishing your findings. This is a unique opportunity to collaborate with various Google teams globally and make substantial impacts by enabling fundamental breakthroughs and pioneering next-generation products.

Key Responsibilities

• Lead advancements in algorithms and model architectures to speed up training and enhance the generalization of deep learning models.
• Develop innovative techniques to make inference with foundational models more efficient, including knowledge adoption and distillation methods.
• Focus on data subset selection and efficient training methods for large datasets.
• Enhance the model deployment pipeline, including pre-training, instruction tuning, and Reinforcement Learning from Human Feedback (RLHF).

If you are passionate about advancing machine learning efficiency and eager to build next-generation intelligent systems, apply today to join Google Research.

Additional Information

Company Name: Google
Job Title: Staff Research Scientist, Machine Learning Efficiency