Principal Engineer, Cloud ML Compute Service at Google
Sunnyvale, TX 75182
About the Job
Minimum qualifications:Bachelor’s degree in Computer Science, Electrical Engineering, or equivalent practical experience.15 years of experience building software and distributed systems.10 years of experience with machine learning algorithms and tools (e.g., PyTorch, TensorFlow, JAX), artificial intelligence, and deep learning models like LLMs, NLP, etc.10 years of experience with hardware and software design, data structures and algorithms, machine learning, and with customer-facing products.10 years of experience with private and public cloud design considerations and limitations in the areas of virtualization, global infrastructure, distributed ML and HPC systems, load balancing, networking, massive data storage, and security.Preferred qualifications:Master's degree in Computer Science, Electrical Engineering, or related field.Demonstrated ability to work cross-functionally, partnering with groups such as Sales, Engineering, Product Management, Product Marketing, UX and UI, brokering trade offs with stakeholders and understanding their needs.Strong analytical and debugging skills.Strong organization, prioritization, and written and verbal communication skills
About the job Google is developing groundbreaking cloud solutions for enterprise companies
Google’s enterprise businesses include the underlying cloud infrastructure through to business user-facing applications
CMCS specifically focuses on leveraging Google’s leadership and expertise in AI/ML to build the best ML platform capable of serving the needs of the most demanding, innovative, and cutting edge ML workloads
The product areas are Google Cloud Platform, AI/ML, compute, storage, and networking infrastructure (IaaS), data and analytics, etc
Google Cloud accelerates every organization’s ability to digitally transform its business and industry
We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably
Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.The US base salary range for this full-time position is $278,000-$399,000 + bonus + equity + benefits
Our salary ranges are determined by role, level, and location
The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations
Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training
Your recruiter can share more about the specific salary range for your preferred location during the hiring process
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits
Learn more about benefits at Google
Responsibilities Design, build, and deploy solutions that leverage GPU, TPU, and highly-scalable hardware and software infrastructure to deliver compelling solutions for GPU/TPU ML workloads.Build strategic alignment with major organizations across Google contributing to the ML landscape to create mutually beneficial joint goals and execute on them.Work across engineering teams that build, design, implement both hardware and software and that span across infrastructure including platforms, chip development, compute, storage, networking and data analytics. Provide leadership for cloud developer technology inside Google and manage collaboration with cross-functional engineering teams to streamline and improve adoption of Google Cloud Platform capabilities, both within Google as well as for the Cloud industry at large.
About the job Google is developing groundbreaking cloud solutions for enterprise companies
Google’s enterprise businesses include the underlying cloud infrastructure through to business user-facing applications
CMCS specifically focuses on leveraging Google’s leadership and expertise in AI/ML to build the best ML platform capable of serving the needs of the most demanding, innovative, and cutting edge ML workloads
The product areas are Google Cloud Platform, AI/ML, compute, storage, and networking infrastructure (IaaS), data and analytics, etc
Google Cloud accelerates every organization’s ability to digitally transform its business and industry
We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably
Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.The US base salary range for this full-time position is $278,000-$399,000 + bonus + equity + benefits
Our salary ranges are determined by role, level, and location
The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations
Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training
Your recruiter can share more about the specific salary range for your preferred location during the hiring process
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits
Learn more about benefits at Google
Responsibilities Design, build, and deploy solutions that leverage GPU, TPU, and highly-scalable hardware and software infrastructure to deliver compelling solutions for GPU/TPU ML workloads.Build strategic alignment with major organizations across Google contributing to the ML landscape to create mutually beneficial joint goals and execute on them.Work across engineering teams that build, design, implement both hardware and software and that span across infrastructure including platforms, chip development, compute, storage, networking and data analytics. Provide leadership for cloud developer technology inside Google and manage collaboration with cross-functional engineering teams to streamline and improve adoption of Google Cloud Platform capabilities, both within Google as well as for the Cloud industry at large.
