Join a pioneering AI Innovation and Research team at Google, focused on accelerating next-generation technologies. This role offers a unique opportunity to shape how billions of users connect and interact with information through cutting-edge AI solutions.

Be part of a dynamic environment where you'll contribute to critical projects at Google's core, with the flexibility to explore diverse challenges across the full technology stack. We are seeking versatile engineers who embody leadership and a passion for tackling complex, evolving problems.

The AI Innovation and Research (AIRX) group specifically empowers Google's Platforms and Devices (P&D) teams by providing essential ML acceleration. We bridge the gap between initial ideas and final deployment, engaging deeply with teams across P&D and GDM to bring AI features to life.

Develop, train, evaluate, and deploy advanced AI/ML models, ensuring they are robust, scalable, and seamlessly integrated into production environments.

Foster strong collaborations with P&D product and research teams to successfully deliver AI-driven features.

Work in close partnership with the Mountain View team, leveraging proximity and established relationships to maximize efficiency and impact.

Identify recurring needs across various projects to build reusable shared infrastructure and models, creating blueprints that expedite future engagements.

Actively transfer valuable AI knowledge and expertise to the P&D product teams, enabling their ongoing success with AI-powered initiatives.

A Bachelor’s degree or equivalent practical experience is required.

Possess at least 5 years of experience in software development using one or more programming languages. Additionally, you should have 3 years of experience in testing, maintaining, or launching software products, along with 1 year focused on software design and architecture.

Demonstrate 3 years of experience in one or more of the following areas: Speech/audio technologies, reinforcement learning, ML infrastructure, or a specialization in another ML field.

Further, provide 3 years of experience with ML infrastructure, including model deployment, evaluation, optimization, data processing, and debugging.