Machine Learning Engineer 3 ( Firefly )

Job Summary

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 through 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 the industry, set technical direction, and mentor other ML engineers.

Job Responsibilities

• Design and evelopment 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 model
• 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

• 6+ years of experience in machine learning, including production-scale deployments.
• 4+ 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.

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

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.