Director, Software Engineering, AI Data Enrichment Pipelines
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Minimum qualifications:
- Bachelor's degree in Computer Science, Electrical Engineering, a related technical field or equivalent practical experience.
- 15 years of experience in software engineering, with 8 years in a technical engineering, systems architecture, or principal-level role.
- 10 years of experience leading and managing software engineering organizations.
- Experience managing other engineering managers.
Preferred qualifications:
- Master’s degree or PhD in Computer Science, Machine Learning, Computer Engineering, or related technical field.
- Deep, specialized technical knowledge of large-scale distributed systems, planet-scale data processing, and complex data orchestration workflows.
- Strong understanding of machine learning lifecycle pipelines, specifically data preparation requirements for post-training, fine-tuning, and reinforcement learning (RLHF).
- Demonstrated ability to engage, influence, and collaborate with senior executive stakeholders (VPs, Directors) across multiple distinct business units.
- Proven track record of leading large, complex, cross-functional engineering organizations (100+ people) in a high-growth, fast-paced environment.
- Proven track record of technical innovation within planet-scale distributed systems and data pipelines.
About the job
AI Foundations is responsible for data at Google, working closely with teams across the company to serve their data needs. The product suite offers end-to-end data management solutions, infrastructure, and foundations for Google products operating at planet-scale, including flagship products such as Gemini, Search, Ads, YouTube, and Geo.
In this role, you will lead the AI Data Enrichment Pipelines team, responsible for the pipelines that support the Search as well as the platforms that produce canonical datasets to train Google’s foundational models. You will serve as the primary technical and strategic bridge between context & inderstanding, DeepMind (GDM), and Search. You will own the strategy for foundry’s data enrichment, an orchestration system that ingest and prep data for model training across all major Google products, ensuring GDM and Search have high-quality, compliant, and highly-optimized post-training datasets.
This role requires an expert-level grasp of technical tradeoffs involving data processing efficiency, training data quality, and systems performance. You will lead a high-performing engineering organization of ~120 people comprising multiple sub-teams and senior technical leads.
The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $307000 - $428000 (USD) + 30% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Define the strategy and lead the consolidation of disparate training data pipelines (text, HTML, images, and video) into a cohesive modularized and multimodal platform, enabling multimodal data journeys.
- Drive technical innovations in dataset management representation, semantic data filtering, and post-training quality loops (e.g., Reinforcement Learning from Human Feedback (RLHF), fine-tuning data prep) where industry standards are constantly shifting.
- Serve as the primary technical liaison engaging with senior leadership across GDM, Search, and AI Foundations to align roadmaps and secure resource commitments.
- Lead the evolution of information retrieval infrastructure toward improved usability, reliability, scalability, efficiency, architectural clarity, and coherence with a focus on AI model development journeys.
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