About this role
The US base salary range for this full-time position is $178,800 - $253,000 + 15% bonus target + equity + benefits determined by role, level, and location. Individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Learn more about benefits at Google .
Position reports to the Google office & may allow for a hybrid schedule per Google policy.
Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.
We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.
Minimum qualifications:
- Bachelor’s degree in Computer Science, Engineering, Computer Information Systems, Mathematics, Physics, or a related field and 5 years of experience in the job offered or in a Research Engineer-related occupation.
- Alternatively, will accept a Master’s degree in Computer Science, Engineering, Computer Information Systems, Mathematics, Physics, or a related field, and 3 years of experience in the job offered or in a Research Engineer-related occupation.
- Position requires 3 years of experience in the following: Statistical data analysis; Python or C++; Infrastructure and pipeline design; Software testing and testing frameworks; and ML / AI experimentation design.
- Apply research ideas to high-impact problems by prototyping, curating datasets, and deploying optimized machine learning systems.
- Architect and implement scalable software libraries and high-quality code in Python or C++ to translate complex research into practical applications.
- Drive high-stake, long-term research projects from ideation to completion by scoping project needs, managing resources, and solving ambiguous problems.
- Train, evaluate, and iterate on deep neural models and reinforcement learning algorithms to continually improve agent performance and achieve research objectives.
- Influence engineering best practices by championing code reviews, mentoring team members, and facilitating clear communication between research and engineering. Communicate research developments, experimental results, and project status clearly to internal teams and the broader external community.