Machine Learning Engineer

Machine Learning Engineer

Changing the world through digital experiences is what Adobe's all about. We give everyone—from emerging artists to global brands—everything they need to design and deliver exceptional digital experiences! We're passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen.

We're on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours!

Adobe Firefly's Applied Science & Machine Learning (ASML) group is looking for a Machine Learning Engineer with a passion for building large-scale data infrastructure to power generative AI. This role focuses on designing, implementing, and optimizing the large scale data systems that drive Firefly's multimodal and editing foundation models. The ideal candidate combines strong software engineering skills with an understanding of ML systems, enabling high-throughput, reliable, and scalable pipelines that accelerate model innovation. As a Machine Learning Engineer at Adobe, you will join an outstanding team of applied scientists and engineers building the future of creativity and digital experiences. You'll work across data, infra, and model optimization teams to transform applied research pipelines into production systems while ensuring our data ecosystem is fast, reproducible, and future-ready for large-scale generative AI development.

Job Responsibilities

• Build scalable data pipelines: Design, implement, and maintain data ingestion, preprocessing, and transformation workflows for multimodal datasets (image, text, and structured signals) that support large-scale training and fine-tuning.
• Optimize performance and throughput: Improve pipeline efficiency and scalability using distributed data systems (e.g., PyTorch DataPipes, Ray, Spark) and cloud infrastructure (Kubernetes, GPUs, object storage).
• Enable reliability and traceability: Implement validation, monitoring, and versioning systems to ensure data quality, correctness, and reproducibility across research and production environments.
• Collaborate across teams: Partner with applied scientists and infra engineers to translate evolving data and model requirements into robust, production-ready systems.
• Accelerate iteration: Develop modular, reusable components that shorten experiment cycles and improve data accessibility for training and evaluation.

What You'll Need to Succeed

• Master's or Ph.D. in Computer Science, AI/ML, or related fields.
• Strong coding skills in Python and experience with ML data toolchains (e.g., PyTorch, TensorFlow, Ray, Spark).
• Experience with data pipeline design, distributed systems, or ML infrastructure.
• Familiarity with containerized and cloud-based environments (e.g., Kubernetes, AWS, GCP).
• Excellent problem-solving and collaboration skills with a focus on delivering high-quality, maintainable systems.
• Eagerness to learn, iterate quickly, and bridge research and production workflows in an applied ML environment.

Preferred Experience

• Experience supporting training and evaluation of large-scale generative or multimodal models.
• Familiarity with data versioning, dataset quality checks, or synthetic data generation workflows.
• Exposure to distributed data loading and streaming systems for large-scale model training.
• Interest in building infrastructure that accelerates applied science innovation and model experimentation across teams.