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 services—both our internally critical and our externally-visible systems—have 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.

To learn more: check out our books on Site Reliability Engineering or read a career profile about why a Software Engineer chose to join SRE.

Deep Mind is at the heart of Alphabet's Artificial Intelligence and Machine Learning (AI/ML) ambitions, where your work directly defines the future of AI. In this role, you will be part of a team that is evolving our methodologies and practices for Site Reliability Engineering (SRE).

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 $207,000-$300,000 + 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

• Develop the technical strategy and roadmap for large, long-term ML projects, prioritizing quick and demonstrable progress and the flexibility to shift focus as business needs evolve.

• Clarify and solve large, open-ended production problems where the solution is initially unclear, delivering solutions that directly accelerate delivery.

• Adapt and evolve SRE practices to be effective for GDM, ensuring production engineering expertise is a strategic enabler of rapid research-led iteration.

• Lead across multiple groups and influence stakeholders with competing priorities to align on outcomes that serve the best interest of GDM's AI mission.

• Initiate all new tasks with agentic tools and lead the team in building long-term agentic muscle memory by developing and sharing new agent skills. Guide the contributions of others and cultivate innovation across multiple teams, managing technical credibility in large-scale programs.

Minimum qualifications

• Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience.

• 8 years of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript).

• 3 years of experience building and developing large-scale infrastructure or distributed systems.

• 3 years of experience in incident management.

• Experience in cross-functional collaboration and stakeholder management.

Preferred qualifications

• Master's degree in Computer Science or Engineering.

• 3 years of experience with Machine Learning Algorithms, Machine Learning Architecture, Machine Learning Infrastructure.

• 3 years of experience in tactical planning.

• 2 years of experience with machine learning algorithms and tools, artificial intelligence, deep learning.

• Experience with Non-abstract Large Systems Design.

• Experience in a Site Reliability Engineering role.