Senior Software Engineering Manager, AI Data

JobsCloseBy Editorial Insights

Google is seeking a Senior Software Engineering Manager, AI Data in London to lead multiple ML-focused teams delivering high-scale infrastructure, model deployment and optimization, with responsibilities spanning strategy, roadmaps, and people leadership. The role requires 8+ years software development, 7+ years guiding ML design and infrastructure, and 5+ years in technical leadership and people management; preferred are advanced degrees and experience in matrixed organizations. You’ll set cross-team priorities, mentor engineers, and shape long-term vision across AI, NLP, and distributed systems. To apply, tailor your resume to show measurable impact in large-scale ML deployments, cross-functional leadership, budget stewardship, and collaboration across sites; prepare to discuss strategy, architecture and people development.

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 8 years of experience with software development.
  
• 7 years of experience leading technical project strategy, ML design, and optimizing industry ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 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 in a technical leadership role.
• 5 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.
  
• 5 years of experience working in a complex, matrixed organization.
  

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.

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.

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 long-term technical vision and roadmap within, and often beyond, the scope of your teams. Evolve the roadmap to meet anticipated future requirements and infrastructure needs.
  
• Oversee systems designs within the scope of the broader area, and review product or system development code to solve ambiguous problems.
  
• Drive technical project strategy, lead large-scale ML infrastructure optimization, and oversee the design and implementation of solutions across multiple specialized ML areas.