Software Engineer, Network Infrastructure

2 Minutes ago • 2 Years + • Network Engineering • $141,000 PA - $202,000 PA

Job Summary

Job Description

Google's Software Engineers develop next-generation technologies for billions of users. This role in the ML, Systems, & Cloud AI (MSCA) organization focuses on designing, implementing, and managing hardware, software, machine learning, and systems infrastructure for Google services and Google Cloud. Responsibilities include writing development code, leading design reviews, reviewing code, triaging system issues, and analyzing network performance, prioritizing security, efficiency, and reliability.
Must have:
  • Bachelor’s degree or equivalent practical experience
  • 2 years of experience with software development or 1 year with an advanced degree in an industry setting
  • 2 years of experience with developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage or hardware architecture
  • Experience with C++ and systems infrastructure
Good to have:
  • Master's degree or PhD in Computer Science or related technical fields
  • 2 years of experience with data structures or algorithms
  • Experience developing accessible technologies
  • Experience in distributed systems
Perks:
  • Bonus
  • Equity
  • Benefits

Job Details

Minimum qualifications:

  • Bachelor’s degree or equivalent practical experience.
  • 2 years of experience with software development or 1 year of experience with an advanced degree in an industry setting.
  • 2 years of experience with developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage or hardware architecture.
  • Experience with C++ and systems infrastructure.

Preferred qualifications:

  • Master's degree or PhD in Computer Science or related technical fields.
  • 2 years of experience with data structures or algorithms.
  • Experience developing accessible technologies.
  • Experience in distributed systems.

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. The ML, Systems, & Cloud AI (MSCA) organization designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.

We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.

Responsibilities

  • Write product or system development code.
  • Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.
  • Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
  • 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.
  • Experiment on testbeds and subsequent analysis of network performance.

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