Senior Research Scientist, Machine Learning Theory, Google Research

Minimum qualifications:

• PhD degree in Computer Science, a related field, or equivalent practical experience.
  
• 2 years of experience leading a research agenda.
  
• One or more scientific publication submission(s) for conferences, journals, or public repositories.
  
• Experience in theoretical computer science and statistical methods.
• Experience in algorithms and statistical learning.
  

Preferred qualifications:

• 2 years of coding experience in C++ and Python.
  
• 1 year of experience leading research efforts and influencing other researchers.
  
• Experience working on large-scale machine learning systems.
  
• Excellent programming skills and experience with machine learning frameworks (e.g., TensorFlow, PyTorch).

About the job

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.

The Learning Theory team at Google is dedicated to advancing the theoretical foundations of machine learning. Its mission is to address fundamental learning theory problems significant to Google.

In this role, you will have expertise in areas including learning theory, statistical learning theory, optimization, decision making under uncertainty, reinforcement learning, and theory and algorithms in general. Our mission is to foster a principled understanding of machine learning techniques and to leverage this knowledge in designing novel and highly effective algorithms. We aim to deploy these algorithms to achieve significant impact on Google, the academic community, and the scientific field of machine learning.

Google Research is building the next generation of intelligent systems for all Google products. To achieve this, we’re working on projects that utilize the latest computer science techniques developed by skilled software developers and research scientists. Google Research teams collaborate closely with other teams across Google, maintaining the flexibility and versatility required to adapt new projects and foci that meet the demands of the world's fast-paced business needs.

The US base salary range for this full-time position is $161,000-$239,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Conduct theoretical research in areas such as optimization, statistical learning theory, deep learning, online learning and reinforcement learning, and other related ML Theory fields.
• Design and analyze novel algorithms with provable guarantees, and validate their effectiveness through experiments.
  
• Collaborate with engineers to translate your theoretical insights into practical solutions for Google's products, including Gemini.
  
• Publish and present findings in conferences and journals in machine learning, statistics, and optimization.
  
• Contribute to shaping Google's research agenda.