Software Engineering Manager II, AI/ML, Google Cloud

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 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).
  
• 3 years of experience in a technical leadership role; overseeing projects, with 2 years of experience in a people management, supervision/team leadership role.
  

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  
• 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.

About the job

The AI and Infrastructure team works on the world’s toughest problems, redefining what’s possible and the possible easy. We empower Google customers by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Googler Cloud customers, and billions of Google users worldwide. We’re at the center of amazing work at Google by being the “flywheel” that enables our advanced AI models, delivers computing power across global services, and offers platforms that developers use to build services.

In AI and Infrastructure, we shape the future of hyperscale computing by inventing and creating world-leading future technology, and drive global impact by contributing to Google infrastructure, from software to hardware (including building Vertex AI for Google Cloud). We work on complex technologies at a global scale with key players in the AI and systems space. Join a team of talented individuals who not only work together to keep data centers operating efficiently but also create a legacy of driving innovation by building some of the most complex systems technologies.

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 $197,000-$291,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. 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

• Set and communicate team priorities that support the broader organization's goals. Align strategy, processes, and decision-making across teams.
  
• Set clear expectations with individuals based on their level and role and aligned to the broader organization's goals. Meet regularly with individuals to discuss performance and development and provide feedback and coaching.
  
• Develop the mid-term technical vision and roadmap within the scope of your (often multiple) team(s). Evolve the roadmap to meet anticipated future requirements and infrastructure needs.
  
• Design, guide and vet systems designs within the scope of the broader area, and write product or system development code to solve ambiguous problems.
  
• 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.