Implement and enhance evaluation pipelines, metrics, and visualization tools to assess multiple aspects of AI models including quality, performance (e.g., latency, power), safety, and efficiency.

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.

Write quality, well-tested, and maintainable code, following Google's engineering best practices and coding standards.

Identify opportunities and implement solutions to improve the reliability, scalability, and efficiency of model evaluation processes.

Contribute to technical documentation, playbooks, and best practices for the team and users of the evaluation platform.

Bachelor’s degree or equivalent practical experience.

2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.

1 year 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 a related Machine Learning (ML) field.

1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).