Senior Cloud GPU Engineer - Scale AI Infrastructure

GOOGLE ASIA PACIFIC PTE. LTD.; Singapore; On-site; SGD 150,000 - 210,000; Full time

Job summary

Google Asia Pacific PTE. LTD. in Singapore is seeking a Senior Software Engineer for the Cloud GPU team to scale AI infrastructure and accelerate GPU integrations within Google Cloud.

You will develop next-generation technologies that enable users to connect with information and run AI workloads at scale. You will work across distributed systems, ML infrastructure, networking and data storage, collaborating with hardware and software teams to improve GPU integration, reliability, and performance

Qualifications

• Bachelor's degree in Computer Science or equivalent practical experience.
• 5 years of software development experience in one or more languages such as Python, C, C++, Java, JavaScript.
• 3 years of testing, maintaining, or launching software products; 1 year in design and architecture.

Responsibilities

• Design and implement highly scalable software solutions and ML infrastructure for GPU integration in Google Compute Engine.
• Write, test, and review complex product code; participate in design/code reviews for best practices.
• Maintain and support accelerator solutions; triage and resolve production issues affecting hardware, network, and services.
• Engage with customers to troubleshoot issues and ensure reliability of AI workloads.
• Contribute to technical documentation and adapt content based on product updates and user feedback.

Skills

Python

C

C++

Java

JavaScript

Education

Bachelor's degree in Computer Science or equivalent

Master's degree or PhD in Computer Science or related field

Job description

Google Asia Pacific PTE. LTD. in Singapore is seeking a Senior Software Engineer for the Cloud GPU team to scale AI infrastructure and accelerate GPU integrations within Google Cloud.

You will develop next-generation technologies that enable users to connect with information and run AI workloads at scale. You will work across distributed systems, ML infrastructure, networking and data storage, collaborating with hardware and software teams to improve GPU integration, reliability, and performance