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:
- Software development using Java, C, C , Python, or Go;
- Designing and applying data structures or algorithms;
- Systems thinking or analyzing technical problems from a broad, systems-level perspective;
- Managing the full lifecycle of applied research or machine learning 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 equity benefits. Transfer compensation is determined algorithmically and is non-negotiable. Learn more about how a transfer may affect your compensation package , how location changes affect compensation , and about benefits at Google at go/benefits .
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. Design and build infrastructure to support next-gen AI features;
- 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; and manage the full deployment lifecycle by contributing to system qualification, monitoring, process automation, and paying down technical debt to improve long-term scalability.
