Software Engineer, Machine Learning, Infrastructure Runtime and API

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
  
• 8 years of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, Javascript).
  
• 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
  
• 3 years of experience with machine learning algorithms and tools (e.g., TensorFlow), or applied ML (e.g., deep learning, natural language processing).
  
• 3 years of experience with low-level programming.
  

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  
• 5 years of experience working in a complex, matrixed organization.
  
• Experienced with low-level programming.
• Understanding of advanced Machine Learning development, with the ability to work full-stack from API design to programming languages, runtimes, compilers, and advanced hardware.
  

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.

With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.

Machine Learning Infrastructure Runtime and API (MIRA)’s mission is to accelerate Machine Learning (ML) for Google and the world by building a modular and scalable ML infrastructure to enable users to develop and execute ML programs seamlessly across frameworks and heterogeneous hardware with performance, usability, and extensibility. We are building the ML infrastructure between the frameworks and hardware as a platform, an operating system for ML programs.

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.

Responsibilities

• Maintain a cross-stack, co-design approach to support Google’s diverse ML ecosystem needs, minimizing use-case specific code paths and turn down investments in maintenance of such fragmented efforts in the current stack.
• Support large-model scale-out training through horizontal scaling.
• Migrate existing frameworks (e.g., TensorFlow, JAX, PyTorch) runtimes (TFExecutor, TFRT, PJRT) and product area custom workflows onto MIRA, minimizing any user disruption.
• Enable integration with open-source components (with frameworks, compilers) in OSS.
• Partner with other Core ML functional areas and Google DeepMind to continuously bring exceptional runtime capabilities to our product areas (e.g., Youtube, Search, etc.).