Staff ML Infrastructure Engineer - GPU & Cloud

Adobe; San Francisco (CA); On-site; USD 212,000 - 307,000; Full time

Job summary

Adobe seeks a senior ML infrastructure engineer for Firefly, its new family of generative AI models. You will design, build, and maintain robust AI/ML infrastructure to support training and deployment of large-scale models, using Kubernetes, Python, and AWS.

Collaborate with data scientists to optimize training pipelines, manage GPU resources, and advance distributed training frameworks such as PyTorch. This high-impact role requires strong problem-solving, communication, and teamwork, and

Qualifications

• 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.

Responsibilities

• Design, develop, and maintain robust AI/ML infrastructure for training and deployment of large-scale models using Kubernetes and Python on AWS.
• Improve distributed training frameworks and GPU utilization for performance and scalability.
• Help train better models by optimizing orchestration, scheduling, and experimentation.
• Collaborate with data scientists and ML researchers to streamline the training pipeline.
• Drive infrastructure innovation to support pioneering ML research and development.

Skills

Python

Distributed training

Problem solving

Communication

Team collaboration

Education

PhD or Master’s in computer science or related field

Tools

Kubernetes

AWS

PyTorch

Job description

Adobe seeks a senior ML infrastructure engineer for Firefly, its new family of generative AI models. You will design, build, and maintain robust AI/ML infrastructure to support training and deployment of large-scale models, using Kubernetes, Python, and AWS.

Collaborate with data scientists to optimize training pipelines, manage GPU resources, and advance distributed training frameworks such as PyTorch. This high-impact role requires strong problem-solving, communication, and teamwork, and