Senior Software Engineer for Performance Platforms
2 weeks ago
Minimum qualifications:
Bachelor's degree or equivalent practical experience. A minimum of 8 years in software development (e.g., C++, Python), alongside expertise in data structures and algorithms. At least 5 years of experience in testing and launching software products, with 3 years dedicated to software design and architecture.
Preferred qualifications:
A Master's degree or PhD in Engineering, Computer Science, or a related technical discipline. 3 years of experience in a technical leadership capacity, guiding project teams and establishing technical direction. 3 years of experience in a complex, matrixed organization with cross-functional or cross-business projects. 3 years of experience in deploying and managing distributed systems on cloud platforms.
Google Cloud empowers organizations to undergo digital transformation across various industries. We provide enterprise-grade solutions that utilize Google's advanced technology and tools, assisting developers in building sustainably.
Clients in over 200 countries and territories rely on Google Cloud as their trusted partner to foster growth and address their most pressing business challenges.
The US base salary range for this full-time position is $189,000-$284,000 + 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 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.
Responsibilities:Facilitate the next generation of accelerated computing platforms (TPU/GPU) for Machine Learning (ML) clients through efficiency, performance, and reliability analysis and enhancements.
Support the infrastructure necessary for training advanced Large Language Models (LLMs) on Google's new TPU hardware, enabling efficient large-scale training involving thousands of TPUs.
Develop tools and leverage fleet metrics to monitor performance, reliability, and efficiency, as well as to extract metrics from Google's operational workloads.
Encourage the adoption of model/data efficiency techniques.
Utilize new hardware capabilities to improve Machine Learning (ML) model architecture for more effective task resolution, employing strategies to minimize downtime and enhance throughput, thereby boosting overall efficiency.
Design, develop, test, deploy, maintain, and enhance large-scale software solutions.
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