Minimum qualifications:

• Bachelor's degree in a STEM field (e.g., Computer Science, Mathematics, Statistics, etc.) or equivalent practical experience.
   
• Experience with one or more general purpose programming languages: Python, C/C++, Java, etc.
   
• Research experience in machine learning or AI techniques (e.g., open source project, campus lab experience, research internship, publication, etc.).
   

Preferred qualifications:

• Master’s degree in a STEM field (e.g., Computer Science, Mathematics, Statistics) or a related field.
   
• Ability to work and collaborate across multiple teams and work with ambiguity.
   

About the job

This is a 24-month Researcher role designed to give you experience working on challenging problems together with Google Research scientists and engineers in India before pursuing (or making a decision on pursuing) doctoral research.

As a Researcher, you will collaborate with researchers, scientists, and engineers at Google Research working on fundamental research and applied problems in a wide range of areas in Computing. You will have a range of opportunities from conducting fundamental research to contributing to products and services and conducting research that contributes to human-centered AI and catalyzes AI for Social Good. We encourage our Researchers to publish their work externally.

Google Research addresses challenges that define the technology of today and tomorrow. From conducting fundamental research to influencing product development, our research teams have the opportunity to impact technology used by billions of people every day.

Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field -- we publish regularly in academic journals, release projects as open source, and apply research to Google products.

Responsibilities

• Work with research mentors to formulate research projects or applications of machine learning.
   
• Conduct research and publish it.
   
• Implement algorithms, experiments, or human-computer interfaces using frameworks (e.g., TensorFlow).
   
• Learn and understand research in machine learning algorithms.
   
• Understand how to use research to drive design decisions.