Software Engineer III, Performance, Machine Learning System Infrastructure

Minimum qualifications Bachelor’s degree or equivalent practical experience. 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree. 2 years of experience with data structures or algorithms. Experience with computer architecture, debugging, performance analysis, and benchmarking and experience with software development using C++. Preferred qualifications Master’s degree or PhD in Computer Science, Computer Engineering, Electrical Engineering or a related field. Experience with Python, TensorFlow, and distributed computing. Experience with architecture analysis and optimization, including system architecture, architecture modeling, system modeling or other similar experience. Experience with distributed systems or a related field. Experience with machine learning. Experience with applications aiming TPU or other ML accelerators. About the job 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. In this role, you will improve overall performance across key Machine Learning (ML) workloads as a TensorFlow model, software stack accelerator software/compiler, and hardware (e.g., TPU, GPU and other ML accelerators). You will interact with product groups, ML practitioners, software teams, and hardware/architecture teams. You will develop custom software tools for benchmarking, profiling, analysis, and reporting. Google is an engineering company at heart. We hire people with a broad set of technical skills who are ready to take on some of technology's greatest challenges and make an impact on users around the world. At Google, engineers not only revolutionize search, they routinely work on scalability and storage solutions, large-scale applications and entirely new platforms for developers around the world. From Google Ads to Chrome, Android to YouTube, social to local, Google engineers are changing the world one technological achievement after another. The US base salary range for this full-time position is $136,000-$200,000 + 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 Execute and report on performance of key ML workloads and the overall ML accelerator fleet. Build tools to profile and analyze results, and to evaluate future accelerator chips. Support new-product introduction with quantitative performance data. Debug an under-performing machine learning model on existing or future hardware. Identify and correct performance bottlenecks throughout the flow and drive optimizations to ML infrastructure to improve the efficiency of the ML accelerator fleet. #J-18808-Ljbffr Google