Software Engineer III, Machine Learning, Google One

Key Skills

Machine learning techniques, Data Analytics, Effective communication skills

Job Description

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
• 2 years of experience with data structures or algorithms in either an
• academic or industry setting.

Preferred qualifications:

• Experience with Data Analytics, Privacy Data Handling, Privacy Design Document, and Mathematical Optimization.
• Understanding of Machine Learning (ML), both theoretical and practical.
• Excellent software engineering and problem-solving skills through programming.
• Excellent communications skills.

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.

Google One (G1) is Google’s membership rooted in storage and provides access to premium features. Our team works across multiple product areas to personalize user experiences through Machine Learning.

Responsibilities

• Write product or system development code.
• Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
• Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
• 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.
• Implement solutions in one or more specialized Machine Learning (ML) areas, utilize ML infrastructure, and contribute to model optimization and data processing.