Sr Manager, Machine Learning Engineering
Adobe, San Jose, CA, United States
Manager For Applied Research At Adobe
Adobe Firefly Foundry is a managed generative-AI service purpose-built for enterprises that need to scale content creation without compromising brand safety or workflow efficiency. By letting customers train private Firefly models on their own intellectual propertyunder strict data isolation and copyright indemnificationand deploy those models directly into Adobe Creative Cloud, Experience Cloud, and proprietary platforms via Firefly Services APIs, Firefly Foundry closes the gap left by consumer-grade GenAI tools and empowers brands to deliver ten-times the creative output at professional quality.
As a Manager for Applied Research at Adobe, you will be the technical and strategic steward of this platform, translating state-of-the-art research into secure, production-ready capabilities, championing them in customer and industry settings, and building the world-class team that will turn vision into reality.
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:
Master's/Ph.D. in CS, Data Science, Engineering, AI/ML, or related fieldor 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.
Adobe Firefly Foundry is a managed generative-AI service purpose-built for enterprises that need to scale content creation without compromising brand safety or workflow efficiency. By letting customers train private Firefly models on their own intellectual propertyunder strict data isolation and copyright indemnificationand deploy those models directly into Adobe Creative Cloud, Experience Cloud, and proprietary platforms via Firefly Services APIs, Firefly Foundry closes the gap left by consumer-grade GenAI tools and empowers brands to deliver ten-times the creative output at professional quality.
As a Manager for Applied Research at Adobe, you will be the technical and strategic steward of this platform, translating state-of-the-art research into secure, production-ready capabilities, championing them in customer and industry settings, and building the world-class team that will turn vision into reality.
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:
Master's/Ph.D. in CS, Data Science, Engineering, AI/ML, or related fieldor 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.