Sr Machine Learning Engineer, Adobe Firefly Services

Adobe; San Jose (CA); On-site; USD 183,300 - 265,350; Full time

Job summary

Adobe is seeking a Senior Machine Learning Engineer for its Firefly Generative AI Services team. This role focuses on designing and developing scalable generative AI systems that power Adobe's core products.

The ideal candidate will have significant experience in machine learning and leadership, particularly with GPU-intensive environments. In addition, you will optimize pipelines for performance and mentor other engineers, fostering innovation within the organization.

Competitive compensation is offered, reflecting the high demand for skills in this area.

Qualifications

• 4-7+ years of experience in machine learning with production-scale deployments.
• Experience leading large-scale, GPU-intensive GenAI systems.
• Good understanding of generative model architectures.

Responsibilities

• Design ML workflows for enterprise-scale model customization.
• Collaborate with Adobe Research for model inference strategies.
• Build and optimize GPU-accelerated pipelines.

Skills

Machine Learning

GenAI frameworks

Leadership

Communication

GPU Management

Education

MS or PhD in Computer Science or related field

Tools

PyTorch

CUDA

Kubernetes

Job description

Job Description

Adobe Firefly's Generative AI Services team is seeking Senior Machine Learning Engineers for our GenAI Services area. In this high-impact role, you will work with a team of talented engineers building scalable, high-performance generative AI systems that power features across Adobe products such as Firefly, Photoshop, Illustrator, Express, Stock, and Premiere.

You will design and develop efficient inference pipelines, optimize models for latency and throughput at inference, and build APIs and ecosystems that integrate both Adobe’s first-party and third-party generative models into Adobe's suite of products for individual and enterprise customers.

You will tackle Adobe's most complex engineering challenges at the forefront of the industry, set technical direction, and mentor other ML engineers.

Job Responsibilities

• Design and development of core GenAI services and APIs that integrate a wide range of generative models into Adobe's flagship products.
• Design and build ML workflows for enterprise-scale model customization, serving, and ecosystem integration.
• Collaborate with Adobe Research and other model developer teams with a focus on model inference strategies and productization.
• Build and optimize GPU‑accelerated pipelines for both customized model training and inference, prioritizing performance, scalability, and reliability.
• Foster a culture of innovation, technical excellence, and continuous improvement across the organization.

What You’ll Need to Succeed

• MS or PhD in Computer Science, Machine Learning, or a related field—or equivalent industry experience.
• 4‑7+ years of experience in machine learning, including production‑scale deployments.
• 2+ years of experience leading large‑scale, GPU‑intensive GenAI systems (training, inference, and optimization).
• Experience with GenAI frameworks and tools such as PyTorch, CUDA, Triton, TensorRT, Nvidia Dynamo, and Python.
• Good understanding of generative model architectures, including diffusion models, transformers, and GANs.
• Good communication and leadership skills, with a track record of driving alignment in matrixed organizations.

Preferred Qualifications

• Experience with model serving, inference, orchestration, and GPU resource management in large‑scale environments.
• Hands‑on expertise in Kubernetes, distributed systems, and MLOps platforms.

Compensation

Expected Pay Range: $151,800 – $265,350 annually in the U.S. (California: $183,300 – $265,350; Washington: $165,600 – $239,725).

EEO Statement

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 protected characteristic. Learn more.