Engineering Manager, GCE VMs, 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).
  
• 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 Computer Science or related technical field.
• 3 years of experience working in a complex, matrixed organization.
• Experience with large-scale AI/ML infrastructure services.
• Experience programming or working with GPUs or other hardware accelerators.
• Experience with Computer Architecture, Operating Systems and Virtualization.
• Experience building and operating very big, large-scale services in a Cloud environment.

Like Google's own ambitions, the work of a Software Engineer (SWE) goes way beyond just Search. SWE Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of engineers. You not only optimize your own code but make sure engineers are able to optimize theirs. As a SWE Manager you manage your project goals, contribute to product strategy and help develop your team. SWE teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day. Operating with scale and speed, our exceptional software engineers are just getting started -- and as a manager, you guide the way.

Machine Learning has revolutionized the IT industry by enabling new scenarios that were previously unthinkable (LLMs, face recognition, voice recognition, etc). This revolution has been made possible by the introduction of Hardware Accelerators capable of the raw processing power required for training and serving ML models.

Google Cloud offers a sophisticated product lineup in the AI/ML infrastructure space. In this product lineup, Google Compute Engine (GCE) virtual machines (VMs) that come with GPUs/TPUs attached are one of the most foundational product lines. Customers manage our VMs directly through the instances APIs or indirectly through Kubernetes or other managed.

The GCE accelerator team owns the GCE VM families that come with accelerators attached. This includes the A3 and A4 VM families as well as G2 and GPU-based N1 VMs.

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.

• Set and communicate team priorities that support the 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 organization's goals. Meet regularly with individuals to discuss performance and development and provide feedback and coaching.
• Develop the mid-term technical goal and roadmap within the scope of multiple teams. Evolve the roadmap to meet anticipated future requirements and infrastructure needs.
• Design, guide and vet systems designs within the scope of the area, and write product or system development code to solve ambiguous issues.
• Review code developed by other engineers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).