Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 2 years of experience with one or more of the following: building AI agents, building Large Language Model (LLM) applications, ML infrastructure, or specialization in another ML field.
- 2 years of experience with software development in C .
- 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
- Experience designing, building, and maintaining large-scale distributed systems.
Preferred qualifications:- Master's degree or PhD in Computer Science or related technical fields.
- 2 years of experience with data structures and algorithms.
- Experience developing accessible technologies.
- Experience with Security.
About the jobIn this role, you will own mission-critical work that powers high-scale data processing. You won't just maintain the systems; you will boost data efficiency and expand the entity graph to empower enterprise teams globally. You will manage a massive technical surface area from cloud customer features to infrastructure delivering solutions that protect the world's largest enterprises in a hyper growth area.
The US base salary range for this full-time position is $147,000-$211,000 bonus equity benefits. Our salary ranges are determined by role, level, and location. 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 benefits at Google .
Responsibilities - Write product or system development code.
- Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
- Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
- Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
- Implement solutions in one or more specialized ML areas, utilize ML infrastructure, and contribute to model optimization and data processing.
