About the job
Join our team as a Senior Manager, Machine Learning Engineering and lead the technical strategy for generative AI at Adobe Firefly Foundry. Drive innovation in large-scale data infrastructure, mentor a world-class team, and shape the future of AI-powered content creation. Make a real impact in a cutting-edge, collaborative environment.
Responsibilities
Lead and grow a distributed team focused on training data for Generative AI.
Define and execute strategy for acquiring, processing, curating, annotating, versioning, and ensuring quality of large-scale datasets for Adobe Firefly.
Build and maintain scalable, efficient data pipelines across the training data lifecycle.
Collaborate with researchers, ML engineers, and PMs to align on data needs for new models.
Champion data diversity, bias mitigation, and ethical AI practices.
Evaluate and integrate tools and methodologies to 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.
Qualifications
Minimum
Master’s/Ph.D. in CS, Data Science, Engineering, AI/ML, or related field—or equivalent experience.
5+ years in engineering leadership, managing data infrastructure or ML data ops teams.
Deep understanding of ML data lifecycles, especially for generative models (GANs, diffusion).
7+ years building and optimizing large-scale ETL/ELT pipelines and data warehousing.
Expertise in data quality, governance, versioning, and annotation platforms.
Proficiency with cloud data tech (AWS, Azure, GCP, Spark, Databricks, Snowflake).
Strong grasp of data privacy, security, and ethical AI principles.
Excellent leadership and cross-functional collaboration skills.
Experience with large-scale image/video datasets.
Preferred
Familiarity with MLOps tools (e.g., DVC), annotation platforms, and compliance standards.
Experience in computer vision, multimedia, or creative content data ecosystems.
Contributions to open-source tools, publications, or patents in data management.
Proven success supporting generative model training with robust data solutions.