Minimum qualifications:- Bachelor's 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 Software Engineer-related occupation.
- Position requires 2 years of experience in the following:
- Applying machine learning, statistics, or diffusion model theory in applied research;
- Software development using Java, C, C , Python, or Go;
- Designing and applying data structures or algorithms;
- Managing the full lifecycle of applied research projects from proof-of-concept to implementation; and
- Data analysis and synthesis to generate solutions or evaluate outcomes for machine learning applications.
About the jobThe US base salary range for this full-time position is $149,400 - $211,000 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, CA office & may allow for a hybrid schedule as per Google policy.
Responsibilities- Apply research to high-impact problems by prototyping GenAI solutions, curating datasets, and building ML pipelines for generative media, multimodal understanding, and reinforcement learning;
- Develop and test robust product code, performing comprehensive testing that includes integration, performance, and security to ensure system quality and reliability;
- Collaborate with peers through rigorous design and code reviews to enforce best practices, improve system testability, and ensure overall efficiency and accuracy;
- Triage and resolve complex system issues by debugging, analyzing root causes, and implementing solutions to optimize hardware, network, and service operations.
- Create and maintain technical documentation and educational materials, adapting content based on product updates and user feedback to ensure clarity and relevance. Manage the full deployment lifecycle by contributing to system qualification, monitoring, process automation, and paying down technical debt to improve long-term scalability.
