Join Google's innovative engineering team and shape the future of how billions interact with information. This role is central to developing cutting-edge technologies that span massive-scale systems and diverse applications. As a versatile engineer with leadership potential, you will tackle complex challenges across the full technology stack, driving progress in areas like artificial intelligence and large-scale systems. Be part of a fast-paced environment where new problems and opportunities for growth are constant.

The Geo team is dedicated to creating the world's most accurate and comprehensive mapping experiences through products like Maps, Earth, and Street View. Serving over a billion users monthly, the team not only enhances user navigation and exploration but also empowers developers through Google Maps Platform. This involves solving intricate computer science problems, crafting intuitive user interfaces, and deepening our understanding of the physical world.

Define the long-term machine learning strategy, including the modeling roadmap and shared objectives, in collaboration with partners. Lead, mentor, and foster the growth of your engineering team.

Architect and optimize sub-second ML/LLM inference and agentic reasoning systems for real-time intent resolution, adaptive follow-up questions, and transparent moderation.

Spearhead the integration of fragmented models into cohesive, unified joint-modeling architectures.

Champion scalable evaluation frameworks, encompassing structured and unstructured data, active learning, and synthetic data generation to maximize model performance.

A Bachelor's degree or equivalent practical experience is required, alongside 8 years of software development expertise.

Possess 5 years of experience in ML design and end-to-end machine learning lifecycle management, including deployment, evaluation, data processing, and debugging. Additionally, 5 years of experience in at least one of the following areas is necessary: Speech/audio technology, reinforcement learning, ML infrastructure, or a specialized ML field.

Demonstrated experience in a technical leadership capacity, including managing engineering teams. Familiarity with Generative AI technologies (e.g., LLMs, diffusion models), their development workflows, and use cases is also essential.

Preferred qualifications include a Master's or PhD in a quantitative field like Machine Learning, AI, or Computer Science. Significant experience with data structures, algorithms, and designing automated evaluation frameworks for generative AI and ML systems is advantageous. Experience in people management within cross-functional settings and the ability to influence diverse stakeholder groups are highly valued.