Manager, Applied Science for Data

AdobeSan Jose, CA
$172,500 - $306,625

About The Position

The Firefly Foundry team is seeking an experienced Applied Science Manager to lead and evolve the training data ecosystem that powers our generative AI models for content synthesis and editing across multiple modalities, including imaging and video. This role is pivotal to building and scaling the science, pipelines, and data strategies essential to developing the next generation of creative tools. We are looking for candidates with a strong foundation in applied research, data algorithms, large-scale data pipelines, and machine learning, paired with a passion for enabling cutting-edge AI research and product development. As a Manager for Applied Science at Adobe, you will develop novel data-centric solutions that advance creativity through features in Adobe’s products, reaching millions of people worldwide.

Requirements

  • Master’s or Ph.D. in Computer Science, Data Science, Engineering, AI/ML, or a related field — or equivalent experience.
  • 5+ years of experience in engineering leadership.
  • Deep understanding of ML data lifecycles, particularly for generative models.
  • Experience in the science and algorithms underpinning data for generative AI.
  • Experience with synthetic data creation and curation.
  • Strong grasp of data privacy, security, and ethical AI principles.
  • Excellent leadership and cross-functional collaboration skills.
  • Experience working with large-scale image and video datasets.

Responsibilities

  • Lead and grow a distributed team focused on training data for generative AI.
  • Define and execute the strategy for acquiring, processing, curating, annotating, versioning, and ensuring the quality of large-scale datasets for Custom Foundation Models.
  • Build and maintain scalable, efficient data pipelines across the training data lifecycle.
  • Collaborate with researchers, ML engineers, and product managers to align on data needs for foundation models.
  • Champion data diversity, bias mitigation, and ethical AI practices.
  • Evaluate and integrate tools and methodologies that enhance infrastructure and workflows.
  • Influence model development through insights on data quality and availability.
  • Provide technical leadership and foster a culture of innovation.
  • Partner across teams to align data priorities and share best practices.

Benefits

  • comprehensive benefits programs
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