Senior Staff Software Engineer, Storage AI/ML Benchmarks
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Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 8 years of experience in software development, and with data structures/algorithms.
- 5 years of experience with design and architecture; and testing/launching software products.
- 5 years of experience with distributed systems, including the design and implementation of scalable and reliable distributed storage solutions.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 5 years of experience in a technical leadership role leading project teams and setting technical direction.
- 3 years of experience working in an organization involving cross-functional or cross-business projects.
- Experience with ML algorithms (e.g., TensorFlow), model tuning, and infrastructure development.
- Knowledge of storage systems, performance optimization, and Machine Learning workloads.
- Ability to design, execute, and analyze benchmarks for storage systems serving Machine Learning applications.
About the job
Google Cloud's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. 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 Cloud's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. You will anticipate our customer needs and be empowered to act like an owner, take action and innovate. 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.
The Google Customer Solutions (GCS) AI/ML Infrastructure team is developing a suite of benchmarks focused on key performance indicators such as throughput, latency, and scalability. These benchmarks will be engineered for reproducibility and transparency, utilizing standardized datasets and methodologies to enable accurate comparisons and facilitate performance analysis for diverse AI/ML applications. This initiative goals to provide valuable data-driven insights to both GCP customers and internal development teams, enabling informed decision-making and continuous optimization of GCS for evolving AI/ML workloads. The team will engage with the open-source community to identify performance issues and contribute to the enhancement of AI/ML software, ensuring optimal utilization of GCS capabilities.
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 $237,000-$337,000 + 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
- Serve as the subject matter expert on AI/ML workloads, guiding GCP Storage teams on performance optimization and best practices.
- Lead the design, development, and tuning of benchmarks that assess GCS performance for diverse AI/ML applications.
- Engineer tools and automation to enable reliable benchmark execution and performance tuning across GCP Storage.
- Publish comprehensive benchmark data and performance metrics to inform both internal teams and GCP customers.
- Influence the broader industry to adopt GCS benchmarks as standards and establish GCS as the leading storage solution for AI/ML.
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