Minimum qualifications:
- Bachelor's degree in Computer Science, similar technical field of study, or equivalent practical experience.
- 15 years of experience as a software engineer.
- Experience working with GPUs and other hardware accelerators.
- Experience working with ML Frameworks (PyTorch, JAX, or TensorFlow).
- 20 years of professional experience.
- Experience in optimizing machine learning models.
- Experience writing custom kernels (CUDA, Pallas, etc.).
- Experience with ML workload performance profiling and analysis.
- Excellent communication and people skills, with the ability to effectively collaborate with customers and internal teams.
- Strong programming skills in Python or C/C++.
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About the job
Google Cloud is searching for a highly skilled and motivated Principal Engineer to optimize machine learning model performance for our customers and help them achieve maximum model performance for large-scale training and inference through tuning and optimization at both software and hardware levels. In this pivotal role, you will collaborate closely with customers, write custom kernels, and develop custom solutions to meet their unique model performance requirements.
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 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
- Lead efforts to optimize machine learning models for speed, memory efficiency, and accuracy through experimentation with different architectures, hyperparameters, and optimization techniques.
- Accelerate model training and inference by identifying and implementing software and hardware optimizations, which may include profiling code, optimizing data pipelines, and working with specialized accelerators (GPUs, TPUs, Terrarium, etc.).
- Translate customer requirements into technical solutions by working closely with them to understand their needs. This includes presenting technical findings and recommendations.
- Identify bottlenecks and areas for improvement by developing and utilizing performance analysis tools.