Staff Site Reliability Engineer, DeepMind SRE
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.
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.
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.
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.
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.