Staff Machine Learning Engineer – ML Frameworks

Adobe

Staff ML Engineer building Kubernetes, Python, and GPU infrastructure for Adobe Firefly generative AI models. Improving distributed training, orchestration, scalability, and experimentation.

Posted 9/2/2026full-timeSan Jose • California, Washington • 🇺🇸 United StatesLead💰 $172,500 - $306,625 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and maintaining AI/ML infrastructure solutions, with a strong focus on Python, Kubernetes, and AWS. Proven ability to enhance distributed training frameworks and optimize resource utilization for large-scale AI model deployment.

Highest-signal resume keywords

Python ProficiencyKubernetes ExperienceDistributed PyTorch KnowledgeAI/ML Infrastructure DevelopmentGPU Resource Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI/ML Infrastructure SolutionsDistributed Training FrameworksModel ServingOrchestration and SchedulingAutoML ToolsPython DevelopmentCritical ThinkingAnalytical Problem-SolvingQuantitative AnalysisMachine Learning

Soft Skills

Excellent CommunicationRelationship SkillsTeam Collaboration

Tools & Technologies

KubeFlowMLFlowRaySageMakerPyTorch DistributedMPIMegatronHorovod

Certifications & Qualifications

PhD in Computer ScienceMaster’s in Computer Science

Industry Keywords

AI ModelsMachine Learning ResearchInfrastructure PracticesResource UtilizationScalability

Tech Stack

Tools & technologies

AWSCloudKubernetesPythonPyTorchRay

About the role

Key responsibilities & impact

Design, develop, and maintain robust AI/ML infrastructure solutions supporting 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 out-of-the-box support for FSDP and model parallelism
Improve orchestration and scheduling to train better models
Scale the number of jobs and enable faster experimentation with AutoML and similar tools
Collaborate with data scientists and ML researchers to streamline model training pipelines and ensure efficient resource utilization
Drive innovation in infrastructure practices supporting machine learning research and development

Requirements

What you’ll need

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
Experience with KubeFlow, MLFlow, Ray, SageMaker, or similar (added plus)
Experience with PyTorch distributed, MPI, Megatron, Horovod and other AI training frameworks (added plus)

Benefits

Comp & perks

Annual Incentive Plan (AIP) for short-term incentives
Certain roles may be eligible for a new hire equity award
Comprehensive benefits programs
Equal Employment Opportunity protections
Disability accommodations during the recruiting and application process