Machine Learning Engineer

Our Company

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.

#Fireflygenai

Our compensation reflects the cost of labor across several  U.S. geographic markets, and we pay differently based on those defined markets. The U.S. pay range for this position is $120,700 -- $228,600 annually. Pay within this range varies by work location and may also depend on job-related knowledge, skills, and experience. Your recruiter can share more about the specific salary range for the job location during the hiring process.

At Adobe, for sales roles starting salaries are expressed as total target compensation (TTC = base + commission), and short-term incentives are in the form of sales commission plans.  Non-sales roles starting salaries are expressed as base salary and short-term incentives are in the form of the Annual Incentive Plan (AIP).

In addition, certain roles may be eligible for long-term incentives in the form of a new hire equity award.

State-Specific Notices:

California:

Fair Chance Ordinances

Adobe will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances.

Colorado:

Application Window Notice

If this role is open to hiring in Colorado (as listed on the job posting), the application window will remain open until at least the date and time stated above in Pacific Time, in compliance with Colorado pay transparency regulations. If this role does not have Colorado listed as a hiring location, no specific application window applies, and the posting may close at any time based on hiring needs.

Massachusetts:

Massachusetts Legal Notice

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

Adobe is proud to be an Equal Employment Opportunity employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other applicable characteristics protected by law. Learn more.

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