Like Google's own ambitions, the work of a Software Engineer goes way beyond just Search. Software Engineering 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 Software Engineering Manager you manage your project goals, contribute to product strategy and help develop your team. 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.
With technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally.
Knowledge Catalog is the foundational context engine for the AI era designed as always-on enterprise-wide catalog for AI, it serves as the single reliable source of truth for both human users and AI agents. By bridging the gap between raw data and true business meaning, we power Google's Agentic Data Cloud, enabling AI agents to reason, act, and execute on enterprise data. It provides universal business context and governance for your entire data estate. Data teams and AI developers use Knowledge Catalog to discover data, enforce policies, and retrieve context for both analytics and self-sustaining applications.
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.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $207000 - $301000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Generate critical, innovative ideas and own the architectural direction for highly ambiguous problem spaces.
- Navigate complex organizational structures to influence technical decisions and align outcomes across various Google Cloud products and distinct engineering organizations.
- Apply strong product-thinking to technical challenges. Partner closely with engineering and product managers to define the long-term roadmap and ensure our technical capabilities align with customer and business needs.
- Provide technical guidance, mentorship, and leadership to engineers across the team, elevating the overall capability and velocity of the organization.
- Maintain a direct approach to coding and system design while setting the standard for engineering excellence.
Qualifications
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 8 years of experience leading to ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 3 years of experience in a people management or team leadership role.
- 2 years of experience with GenAI techniques (e.g., Large Language Models (LLMs), Multi-Modal, Large Vision Models) or with GenAI-related concepts (e.g., language modeling, computer vision).
- Experience with software development in one or more general purpose programming languages (e.g., Java, C/C++, or Go, etc.).
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures and algorithms.
- 3 years of experience in a technical leadership role leading project teams and setting technical direction.
- Experience with shipping 0-to-1 AI applications, with a holistic understanding of product, quality, and infrastructure.
- Knowledge of data warehouses, big data, SQL, and data governance.