Minimum qualifications:- PhD degree in Computer Science, Engineering, Computer Information Systems, Mathematics, Physics, or a related field and 2 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 5 years of experience in the job offered or in a Research Engineer-related occupation.
- Position requires 2 years of experience in the following:
- Python for Software Development and Machine Learning.
- Machine learning and reinforcement learning algorithm design.
- Root cause analysis for debugging ML systems.
- Software design and systems architecture for performance and reliability
- ML infrastructure development for model deployment, evaluation, and optimization.
About the jobThe US base salary range for this full-time position is $174,000 - $252,000 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 Mountain View, California office & may allow for a hybrid schedule as per Google policy.
Responsibilities- 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.
