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Staff Software Engineer, AI Foundry Data Quality and Training Data

Google
San Jose, USA
Senior · 8+ years experience
Onsite
USD 207000-300000 / year
Discovered 2 weeks ago

Full Job Posting

Responsibilities

  • Provide technical leadership and people management for a team of Software Engineers delivering AI-ready training datasets and data quality infrastructure.
  • Architect, build, and evolve large-scale distributed data processing pipelines, signal-serving platforms, and dataset observability systems with rigorous reliability and Service Level Objective (SLO) guarantees.
  • Partner closely with peer tech lead managers, Über Tech Leads (UTLs), and DeepMind researchers to drive the end-to-end roadmap for pre-training, post-training, and multimodal training data delivery
  • Design and scale web-scale algorithms and optimization frameworks including crawl targeting, document selection, deduplication, spam/low-value filtering guardrails, and objective functions operating across hundreds of billions of documents.
  • Establish rigorous data quality metrics and automated anomaly detection.

Minimum qualifications:

  • Bachelor's degree or equivalent practical experience.
  • 8 years of experience designing, building, and operating large -scale distributed systems, with a focus on scalability, fault tolerance, and reliability.
  • 2 years of experience in a people management, supervision/team leadership role.
  • Experience working with algorithms and data structures, analysis, and software design.
  • Experience in software engineering principles, design patterns, and system architecture.

Preferred qualifications:

  • PhD degree in Computer Science, Machine Learning, Distributed Systems, Operations Research, or a related technical field.
  • Experience with algorithms and mathematical or system performance improvements.
  • Experience building and operating high-throughput distributed systems on infrastructure or equivalent batch and streaming frameworks.
  • Experience as a tech lead manager leading engineering teams in domains bridging infrastructure and machine learning.
  • Experience with data quality, dataset curation, value-of-data analysis, or training data pipelines for Large Language Models (LLMs) and multimodal foundation models.

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