Staff Software Engineer, Applied AI for Performance Optimization

MINIMUM QUALIFICATIONS:

* Bachelor's degree or equivalent practical experience.

* 8 years of experience programming in C++ or Python.

* 5 years of experience testing, and launching software products.

* 5 years of experience with performance, large scale systems data analysis,

visualization tools, or debugging.

* 3 years of experience with software design and architecture.

* Experience with end-to-end performance analysis for deployed platforms to

understand and mitigate performance bottlenecks.

PREFERRED QUALIFICATIONS:

* Master’s degree or PhD in Engineering, Computer Science, or a related

technical field.

* 8 years of experience with data structures and algorithms.

* 3 years of experience in a technical leadership role leading project teams

and setting technical direction.

* 3 years of experience working in a complex, matrixed organization involving

cross-functional, or cross-business projects.

ABOUT THE JOB:

Google's software engineers develop the next-generation technologies that change

how billions of users connect, explore, and interact with information and one

another. Our products need to handle information at massive scale, and extend

well beyond web search. We're looking for engineers who bring fresh ideas from

all areas, including information retrieval, distributed computing, large-scale

system design, networking and data storage, security, artificial intelligence,

natural language processing, UI design and mobile; the list goes on and is

growing every day. As a software engineer, you will work on a specific project

critical to Google’s needs with opportunities to switch teams and projects as

you and our fast-paced business grow and evolve. We need our engineers to be

versatile, display leadership qualities and be enthusiastic to take on new

problems across the full-stack as we continue to push technology forward.

In this role, you will

• be drive innovation through GenAI methods,
• revolutionizing performance optimization and efficiency across existing fleet
• infrastructure as well as next-generation platforms. You will apply AI/ML or
• other investigative techniques to improve performance/efficiency of key
• applications or the entire fleet. You will be responsible for identifying or
• prototyping new projects, taking them to production. In other words, you will
• make the goal of a “self-driving” autonomous fleet a reality.
• You will be working with a small team of computer architects, compiler and
• efficiency experts, runtime system experts, and AI researchers. You will be
• collaborating with developers up and down the stack, including hardware, kernel,
• networking, and cluster management teams to implement and deploy your changes.
• The AI and Infrastructure team is redefining what’s possible. We empower Google
• customers with breakthrough capabilities and insights by delivering AI and
• Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our
• customers include Googlers, Google Cloud customers, and billions of Google users
• worldwide.
• We're the driving force behind Google's groundbreaking innovations, empowering
• the development of our cutting-edge AI models, delivering unparalleled computing
• power to global services, and providing the essential platforms that enable
• developers to build the future. From software to hardware our teams are shaping
• the future of world-leading hyperscale computing, with key teams working on the
• development of our TPUs, Vertex AI for Google Cloud, Google Global Networking,
• Data Center operations, systems research, and much more.
• Individual pay is determined by factors including job-related skills,
• experience, and relevant education or training.
• US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
• Learn more about benefits at Google
• [https://www.google.com/about/careers/applications/benefits/].
• RESPONSIBILITIES:
• * Develop end-to-end performance analysis for deployed platforms to understand
• and mitigate performance bottlenecks.
• * Identify opportunities and prototype AI/ML-driven approaches to improve on
• existing heuristics.
• * Develop engineering driven methodology to inform the system architecture and
• design of all systems deployed in Google data centers.
• * Work with the infrastructure and application teams to deploy novel solutions
• across Google’s data-center fleet. This could include interacting with
• vendors to bring Google requirements into their roadmaps; and ensure the
• roadmap requirements are well understood and that the capabilities of the
• underlying platform are fully exploited.
• * Design, develop, test, deploy, maintain and improve software systems.