Software Engineering Manager, AI/ML, CodeAI Foundations

About the job

Like Google's own ambitions, the work of a Software Engineer goes 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.

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.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

Germany: €156000 - €159000 (EUR) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Provide technical direction and oversight for AI/Machine Learning initiatives, guiding the team in choosing appropriate approaches for ambiguous problems.
• Design, guide, and vet system designs and ML pipelines, ensuring adherence to Google's engineering practices.
• Serve as a subject matter expert, keeping up with the rapid pace of AI/ML innovation and identifying hidden opportunities or cheaper execution alternatives.
• Build, lead, and develop a high-performing team of Software/ML Engineers, setting clear expectations, providing regular coaching and feedback, and actively investing in the careers of team members.
• Foster a welcoming and collaborative team environment aligned with Google’s values, developing project roadmaps, establishing team goals, setting priorities, and coordinating resources to deliver business impact.

Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience in software development.
• 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.
• 2 years of experience in a people management or team leadership role.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
• Experience developing or managing products that utilize multi-agent systems, agentic workflows, or tool-calling frameworks.
• Experience building or maintaining infrastructure for log analytics, execution trajectories, or automated feedback loops to improve system performance.
• Experience leading engineering efforts across organizational boundaries in a matrixed environment.
• Proven track record of taking AI/ML or NLP prototypes and successfully productionizing them into scalable, developer tools.