Machine Learning Engineer, Adobe Firefly Services

Senior Machine Learning Engineer

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 in building scalable, high-performance generative AI systems—powering features across Adobe products like 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 suite of products that serve 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 of those models.
• 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.