Software Engineer, AI/ML Platform, GPU
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Google will be prioritizing applicants who have a current right to work in Singapore, and do not require Google's sponsorship of a visa.
Minimum qualifications:
- Bachelor’s degree in Computer Science or equivalent practical experience.
- 2 years of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript), or 1 year of experience with an advanced degree.
Preferred qualifications:
- Master's degree or PhD in Computer Science or related technical fields.
- 2 years of experience with data structures or algorithms.
- Experience with Cloud Infrastructure.
- Ability to communicate in both English and Mandarin fluently to support client management in this region.
About the job
The Cloud GPU team is central to AI innovation, dedicated to building and maintaining an industry-leading GPU fleet and AI Platform. Our core mission is to empower Google Cloud's most sophisticated training and inference customers by providing unparalleled computational resources. We are responsible for the entire life-cycle of GPU offerings within Google Cloud, from the initial launch of new GPU families to ensuring their optimal reliability and operational excellence for cutting-edge AI workloads.Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. We're looking for engineers who bring fresh ideas from all areas, including distributed computing, large-scale system design, networking and data storage, artificial intelligence; the list goes on and is growing every day.
Responsibilities
- Write product or system development code.
- 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.
- 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).
- Implement GenAI solutions, utilize ML infrastructure, and contribute to data preparation, optimization, and performance enhancements.
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