Software Engineer, ML Fabric Deployment Acceleration

Minimum qualifications:

Bachelor’s degree or equivalent practical experience.

2 years of experience with coding in C++ and Python, or 1 year of experience with an advanced degree.

2 years of experience with test frameworks.

Preferred qualifications:

Master's degree or PhD in Computer Science, or a related technical field.

2 years of experience with ITS SUT.

Experience developing Integration test frameworks.

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 focused on enabling faster, more scalable, and smoother network deployment processes by providing core integration test infrastructure and supporting other teams in using the framework to create and maintain integration tests.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

Own software development through planning, design, development, testing, maintenance, and improvements.

Work closely with other PLANET/PIE and cross-functional teams on API designs/system integrations and gathering requirements.

Develop core infrastructure used by external teams, and support system integration testing for network deployment automation.

Support delivery of critical network programs to deliver next-generation ML systems.

Manage individual project priorities, deadlines and deliverables.