Staff Software Engineer - AI Data Quality Pipelines

Google LLC; San Jose (CA); On-site; USD 207,000 - 300,000; Full time

Job summary

Google LLC in San Jose seeks a Staff Software Engineer for AI Foundry Data Quality and Training Data. You will lead a team focused on data quality, build scalable data pipelines, and advance datasets powering DeepMind and Gemini.

The role blends leadership and hands-on software engineering across exabytes of data. You will collaborate with DeepMind researchers and other engineers to optimize dataset selection, reliability, and automation.

Qualifications

• 8 years of experience designing, building, and operating large-scale distributed systems, focusing on scalability and reliability.
• 2 years in a people management or team leadership role.
• Experience with algorithms, data structures, and software design.
• Knowledge of software engineering principles, design patterns, and system architecture.

Responsibilities

• Provide technical leadership and people management for a team delivering AI-ready training datasets and data quality infrastructure.
• Architect and evolve large-scale distributed data pipelines and dataset observability with SLOs.
• Collaborate with tech leads and researchers to drive data delivery roadmaps for pre-training and multimodal models.
• Design and scale web-scale algorithms and optimization frameworks across billions of documents.
• Establish rigorous data quality metrics and automated anomaly detection.

Skills

People management

Distributed systems design

Algorithms & data structures

Software architecture

Education

Bachelor's degree or equivalent practical experience

Job description

Google LLC in San Jose seeks a Staff Software Engineer for AI Foundry Data Quality and Training Data. You will lead a team focused on data quality, build scalable data pipelines, and advance datasets powering DeepMind and Gemini.

The role blends leadership and hands-on software engineering across exabytes of data. You will collaborate with DeepMind researchers and other engineers to optimize dataset selection, reliability, and automation.