Staff Site Reliability Engineer, User Protection SRE

Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders; deep expertise in domain. Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.

Minimum Qualifications

• Bachelor's degree in Computer Science or a related technical field or equivalent practical experience.
• 8 years of experience with site reliability engineering focused on building and maintaining scalable, reliable systems.
• Experience developing/launching products/technologies within AI/ML or a related area.
• Experience in designing, analyzing and troubleshooting distributed systems.
• Experience architecting for resilient systems.
  
  

Preferred Qualifications

• 3 years of experience working effectively cross-functionally with proven track record of driving results.
• Experience with AI or passion for the AI space with proficiency in using one or more leading AI platforms.
• Ability to debug, optimize code, and to automate routine tasks.
• Excellent problem-solving approach, coupled with effective communication skills and a sense of ownership and drive.
• Excellent communication skills with the ability to build technical consensus across teams.
• Excellent programming skills in one or more languages (e.g., Java, Python, Go, C ).
  
  

About The Job

Site Reliability Engineering (SRE) combines software and systems engineering to build and run large-scale, massively distributed, fault-tolerant systems. SRE ensures that Google's servicesboth our internally critical and our externally-visible systemshave reliability, uptime appropriate to users' needs and a fast rate of improvement. Additionally SRE's will keep an ever-watchful eye on our systems capacity and performance. Much of our software development focuses on optimizing existing systems, building infrastructure and eliminating work through automation. On the SRE team, you'll have the opportunity to manage the complex challenges of scale which are unique to Google, while using your expertise in coding, algorithms, complexity analysis and large-scale system design. SRE's culture of intellectual curiosity, problem solving and openness is key to its success. Our organization brings together people with a wide variety of backgrounds, experiences and perspectives. We encourage them to collaborate, think big and take risks in a blame-free environment. We promote self-direction to work on meaningful projects, while we also strive to create an environment that provides the support and mentorship needed to learn and grow.

Behind everything our users see online is the architecture built by the Technical Infrastructure team to keep it running. From developing and maintaining our data centers to building the next generation of Google platforms, we make Google's product portfolio possible. We're proud to be our engineers' engineers and love voiding warranties by taking things apart so we can rebuild them. We keep our networks up and running, ensuring our users have the best and fastest experience possible.

The US base salary range for this full-time position is $197,000-$291,000 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

• Build best practices and tooling for managing AI/ML deployments including observability, mitigation capabilities, SLOs and capacity management while continuing to improve deployment velocity.
• Drive the whole life-cycle of service from inception and design, through to deployment, operation and refinement.
• Monitor services once they are live by measuring and tracking availability, latency and overall system health.
• Evolve systems sustainably through mechanisms like automation, and advanced systems by pushing for changes that improve reliability and velocity.
• Lead sustainable incident response and blameless postmortems.
  
  

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