Principal Machine Learning Engineer, Firefly

Adobe•San Jose, CA

About The Position

Adobe Firefly’s Generative AI Services team is seeking a Principal Machine Learning Engineer to serve as the technical lead for our GenAI Services area. In this high-impact role, you will lead 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 architect 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. As a hands-on technical leader, you will tackle Adobe’s most complex engineering challenges, set technical direction, and mentor a growing organization of ML engineers.

Requirements

MS or PhD in Computer Science, Machine Learning, or a related field—or equivalent industry experience.
8+ years of experience in machine learning, including production-scale deployments.
3+ years of experience leading large-scale, GPU-intensive GenAI systems (training, inference, and optimization).
Deep experience with GenAI frameworks and tools such as PyTorch, CUDA, Triton, TensorRT, Nvidia Dynamo, and Python.
Strong understanding of generative model architectures, including diffusion models, transformers, and GANs.
Proven success in leading cross-functional teams through complex, high-stakes initiatives.
Excellent communication and leadership skills, with a track record of driving alignment in matrixed organizations.

Nice To Haves

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

Responsibilities

Lead the development of core GenAI services and APIs that integrate a wide range of generative models into Adobe’s flagship products.
Design and architect ML workflows for enterprise-scale model customization, serving, and ecosystem integration.
Build and optimize GPU-accelerated pipelines for both (customized) model training and inference—prioritizing performance, scalability, and reliability.
Provide hands-on technical leadership, guiding engineers through architecture, design, implementation, and best practices.
Research and evaluate emerging ML and MLOps technologies to enhance engineering velocity and system performance.
Drive cross-functional alignment by partnering with Product Managers, TPMs, and other engineering leaders to define and deliver on the roadmap.
Lead design reviews and set technical standards, ensuring high reliability and maintainability across systems.
Foster a culture of innovation, technical excellence, and continuous improvement across the organization.