Engineering Manager, GenAI Human Data Quality

al 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.

We are building Google-wide first-party tooling for onboarding very large rater workforces to meet the human data needs for all Gemini use cases. Our team builds and maintains platforms, scalable infrastructure services, and applications in support of human computation, enabling the collection, storage, and management of large-scale human-generated or human-augmented datasets used by teams in Google and Alphabet working on GenAI products and services.

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

• Lead a team of Engineers in alignment with Google's manager expectations by delivering results, building a community, and developing people.

• Improve the quality of data collections used for GenAI model training by identifying best practices through quantitative pilot studies and implementing the adoption of these practices by human data collection systems.

• Productionize and standardize methods developed by data scientists for high-quality data and ensure that these metrics are visible to the right stakeholders, meaningful, and actionable.

• Collaborate with horizontal infrastructure teams to monitor and report data quality at every stage of the data collection lifecycle, from collection design through training and model release.

• Provide technical and managerial guidance to the Crowd and Data Platform team.Work cross-functionally with Data Science, Product, UX/UXR, Infrastructure, and Operations teams.