Software Engineering Manager, AI i18n and Evaluations

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:

• Experience in one or more of the following areas: on-device machine learning, mobile algorithms, Large Language Models (LLM)/GenAI evaluations, data collection for ML, and Android development.
• Experience in AI tool chain, evaluation metrics design, and data management techniques.
• Experience leading distributed teams across US and Asia.
• Experience launching one or multiple AI/ML-powered user-facing products.
• Knowledge of Android or Pixel development ecosystem.

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.

You will be responsible for leading team to evaluate the quality for intelligent applications on Pixel and Android, leveraging the latest on-device and server based models. This includes tasks such as frameworks design, evaluation tooling, quality methodologies, metrics design, and data analysis. You will be evaluating data and ML quality for a variety of use cases, working with teams across Google to design and improve infrastructure and processes for Pixel and Android AI development.

The Platforms and Devices team encompasses Google's various computing software platforms across environments (desktop, mobile, applications), as well as our first party devices and services that combine the best of Google AI, software, and hardware. Teams across this area research, design, and develop new technologies to make our user's interaction with computing faster and more seamless, building innovative experiences for our users around the world.

• Develop the local team expertise and projects, mentoring team and removing bottlenecks. Improve team's design, code, and engineering practices.
• Interface with cross-functional and remote teams, engineering managers, and stakeholders to influence cross-team priority and road map.
• Design tools, platforms, frameworks to enhance intelligent applications development, with focus on model evaluation, quality, and performance on Pixel and Android.
• Craft evaluation datasets, metrics, and pipelines to understand, evaluate and improve the behavior of our models, platform and algorithms. Support a multitude of user facing features that rely on the quality and performance of these algorithms, as running on-device and across different interfaces.
• Collaborate with AI feature teams, model developers and researchers to understand evaluation requirements, provide support, and integrate new models and use cases into the evaluation framework.