Staff Software Engineer, Machine Learning Runtime, Google Cloud

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 testing, and launching software products, and 3 years of experience with software design and architecture.
  
• 5 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
  
• 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
  

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  
• 3 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.

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.

The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.

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

• Provide technical leadership on high-impact projects. Manage project priorities, deadlines, and deliverables.
  
• Facilitate alignment and clarity across teams on goals, outcomes, and timelines, influence and coach a distributed team of engineers.
  
• Lead the design and implementation of solutions in specialized ML areas, optimize ML infrastructure, and guide the development of model optimization and data processing strategies.
  
• Migrate existing frameworks (e.g., TensorFlow, JAX, PyTorch) runtimes (e.g., TFExecutor, TFRT, PJRT) and product area custom workflows from TPU to GPU and minimizing any user disruption.
• Support Google’s diverse ML ecosystem needs.