Senior Staff Research Scientist, AI, Google Cloud

3 Months ago • 6-6 Years • Artificial Intelligence • $237,000 PA - $337,000 PA

Job Summary

Job Description

As a Senior Staff Research Scientist, AI at Google Cloud, you will be responsible for developing new Machine Learning (ML) compilation approaches and compiler architecture that effectively handles heterogeneous hardware for efficient ML execution. You will also code rewriting systems and explore generating dedicated hardware accelerators tailored to specific ML model types. You will use ML for automatic compiler generation, optimizing heuristics, and creating custom code-lowering strategies. You will actively contribute to the wider research community by sharing and publishing your findings.
Must have:
  • PhD in Computer Science or equivalent
  • 6+ years of experience in research
  • Experience coding in Python and C++
  • Scientific publications
Good to have:
  • Experience leading research efforts
  • Experience with ML Compilers

Job Details


Minimum qualifications:

  • PhD degree in Computer Science, a related field, or equivalent practical experience.
  • 6 years of experience with research agendas across multiple teams or projects.
  • Experience coding in Python and C++.
  • One or more scientific publication submission(s) for conferences, journals, or public repositories.

Preferred qualifications:

  • 4 years of experience leading multiple research efforts and influencing research direction.
  • 3 years of experience with Machine Learning (ML) Compilers.
  • 2 years of experience coding in Python and C++.

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 Google Cloud AI Research team addresses AI challenges motivated by Google Cloud’s mission of bringing AI to tech, healthcare, finance, retail and many other industries. We work on a range of unique problems focused on research topics that maximize scientific and real-world impact, aiming to push the state-of-the-art in AI and share findings with the broader research community. We also collaborate with product teams to bring innovations to real-world impact that benefits our customers.

The US base salary range for this full-time position is $237,000-$337,000 + 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 .

Responsibilities

  • Develop new Machine Learning (ML) compilation approaches and compiler architecture that effectively handles heterogeneous hardware for efficient ML execution.
  • Code rewriting systems and explore generating dedicated hardware accelerators tailored to specific ML model types.    
  • Use ML for automatic compiler generation, optimizing heuristics, and creating custom code-lowering strategies.

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