We're looking for a Senior Machine Learning Engineer to join our Applied Science Data
Frameworks team. In this role, you'll build the infrastructure that powers large-scale, multimodal
AI training and inference.
You'll work across machine learning, distributed systems, and data engineering to develop
tools and platforms that help teams train and deploy models at scale. Your work will support
systems that process billions of data points across large GPU environments.
If you're motivated by solving complex problems and building systems that enable others to do
their best work, we'd love to connect.
What You'll Do
• Build distributed data loaders to support large-scale training workflows
• Develop data pipelines for ingesting, transforming, and preparing multimodal datasets
• Design batch inference systems for high-volume data processing across GPU
environments
• Improve system performance, scalability, and reliability using distributed computing
tools (e.g., Ray, Spark, DuckDB)
• Implement search and retrieval systems using vector databases and embedding-based
approaches
• Develop and maintain CI/CD workflows, including testing, deployment, and
containerization
• Partner with researchers and engineers to turn model requirements into scalable systems
• Create reusable tools, libraries, and documentation to support teams across the
organization
• Monitor and improve system health, including throughput, latency, and resource
utilization
• Support a collaborative team environment through code reviews and knowledge sharing
What You Bring
• 8+ years of experience building and operating distributed systems or ML infrastructure
in production
• Experience working with large-scale data pipelines or inference systems
• Strong programming skills in Python and a foundation in software engineering principles
• Experience with ML frameworks such as PyTorch or TensorFlow
• Familiarity with distributed computing tools (e.g., Ray, Spark, Dask, or similar)
• Experience working with cloud platforms such as AWS or Azure
• Understanding of MLOps practices, including CI/CD and deployment workflows
• Ability to communicate clearly and collaborate with cross-functional teams
Education
• Master's degree or Ph.D. in Computer Science, Machine Learning, or a related field
(or equivalent practical experience)
Nice to Have
• Experience working with multimodal data (images, video, text)
• Familiarity with vector databases or semantic search systems
#FireFlyGenAI
Expected Pay Range:
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 $172,500 -- $306,625 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.
In California, the pay range for this position is $211,800 - $306,625In Washington, the pay range for this position is $201,000 - $291,150
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.
