Staff Software Engineer, Platform-Aware AutoML

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 8 years of experience in software development, and with data structures or algorithms.
• 5 years of experience in testing, and launching software products, and 3 years of experience with software design and architecture.
• 5 years of experience leading Machine Learning design and optimizing Machine Learning infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning, etc.).
• Experience developing software applications using Python.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, a related technical field, or equivalent practical experience.
• 3 years of experience in a technical leadership role and setting technical direction.
• 3 years of experience in working cross-functionally, or cross-business projects.
• Experience with Machine Learning (ML) based performance work.
• Experience in spanning across various Machine Learning (ML) domains (e.g., Deep Learning, Reinforcement Learning, Neural Networks, Autoregressive Models, etc.).
• Knowledge of computer architecture and performance analysis.

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

Machine Learning models are becoming difficult to get the best performance out of the hardware they run on and use our dynamic computers as efficiently as possible. To solve this, you will be developing a system called Platform-Aware Auto Machine Learning (ML), which will automatically finds the best settings for both the model and the hardware, so we can get the most out of our machines, Platform-Aware AutoML helps researchers, computer architects, and people using Machine Learning work together to make the most of the latest technology.

In this role, you will be responsible for performance and extracting maximum for AI/ML training workloads. You will drive Machine Learning (ML) performance by identifying performance opportunities in Google production and research Machine Learning workloads, landing optimizations.

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 $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. Learn more about benefits at Google.

• Develop Machine Learning job scheduling efficiency to match the best Machine Learning hardware with ML models.
• Conduct research and development to gain understanding and enable business critical Machine Learning workloads (e.g., Search, Ads, LLM, etc.) with Platform-Aware AutoML.
• Establish understanding of the latest Machine Learning platform hardware architecture to enable automated optimization.
• Optimize business critical Machine Learning workloads to gain insights on how these optimizations can be automated at scale.
• Develop, and productionize effective reinforcement learning algorithms to Platform-Aware Auto Machine Learning.