Engineering Manager, AI Data

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 in a technical leadership role; overseeing projects, with 5 years of experience in a people management, supervision/team leadership role.
  

Preferred qualifications:

• Master's degree or PhD in Computer Science, or a related technical field.
  
• 5 years of experience working in a changing organization.
• Experience with ML research and development workflows.
• Experience working with LM/Generative AI infrastructure.

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.

The team's mission is to improve the quality of models that Google releases through its various product offerings by providing tools and services for making easier to reach model quality goals.

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.

• Lead a team of Engineers that scale Data Optimisation techniques improving the performance, fairness, and quality of ML models.
• Work with Research teams as well as ML practitioners to identify, build, and iterate on engineering tools, processing pipelines, data optimization techniques, integration with existing workflows, user interfaces, and supporting users adoption.
• Build the infrastructure ecosystem to support AI researchers and engineers.
• Work in a fast-moving environment with ambiguity.