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# Staff Machine Learning Engineer

- **Company:** Adobe Inc.
- **Location:** Washington, CA, United States
- **Experience:** Expert
- **Salary:** $211,800.0 - $306,625.0
- **Contract:** Permanent contract
- **Skills:** Adobe Creative Cloud, Artificial Intelligence, Amazon Web Services, Extract Transform Load (ETL), Distributed Computing Environment, Python (Programming Language), Machine Learning, Graphics Processing Unit (GPU), Pytorch, Adobe, Kubernetes, Information Technology, Machine Learning Operations
- **Published:** September 5, 2026
- **Apply:** https://startup.jobs/staff-machine-learning-engineer-ml-frameworks-aleu-adobe-systems-europe-li-9873552

## About the Role

* PhD or Master's in computer science or related field and 5+ years of hands-on industry experience

 * Proven proficiency with Python and developing systems, frameworks and SDKs

 * Experience with infrastructure and understanding of model serving, training, orchestration, and management of GPU resources

 * Experience with machine learning and distributed Pytorch

 * Strong critical thinking, analytical and quantitative problem-solving ability

 * Excellent communication, relationship skills and a strong teammate

While not required, it's an added plus if you also have:

 * Experience with KubeFlow, MLFlow, Ray, SageMaker, or similar

 * Experience with Pytorch distributed, MPI, Megatron, Horovod and other AI training frameworks

#FireflyGenAI

## Description

Firefly is the new family of creative generative AI models coming to Adobe products that offers a new way to conceptualize, build, and scale content. It's a natural extension of the technology Adobe has produced over the past 40 years.

At the core of Firefly are our commercially safe AI models trained on hundreds of millions of images owned or licensed by Adobe. We are hiring for a highly strategic and visible role to help evolve these models. This is an opportunity to reach millions of creatives, helping them reinvent the way they work.

What you will Do

 * Design, develop, and maintain robust AI/ML infrastructure solutions to support the training and deployment of large-scale AI models, using Kubernetes and Python on AWS cloud

 * Implement and improve distributed training frameworks leveraging GPUs to improve performance and scalability. Improve resiliency, elasticity, data loading and provide out-of-the-box support for FSDP and model parallelism

 * Help train better models by improving orchestration and scheduling, scaling the number of jobs, faster experimentation with AutoML and similar

 * Collaborate with data scientists and ML researchers to streamline the model training pipeline and ensuring efficient resource utilization

 * Drive innovation in infrastructure practices to support pioneering machine learning research and development


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