Principal Service Engineer, Adobe Firefly

About the role

Adobe Firefly’s Generative AI Services team is seeking a Principal Service Engineer to lead the development and scaling of core GenAI services and APIs that integrate diverse generative models into Adobe’s flagship products. This role involves designing and architecting inference infrastructure for enterprise-scale model customization, serving, and ecosystem integration. You will also provide hands-on technical leadership, guide engineers through architecture, design, implementation, and best practices, evaluate and incorporate emerging MLOps technologies, and drive cross-functional alignment.

Responsibilities

• Lead the development and scaling of core GenAI services and APIs that integrate diverse generative models into Adobe’s flagship products.
• Design and architect inference infrastructure for enterprise-scale model customization, serving, and ecosystem integration.
• Provide hands-on technical leadership, guiding engineers through architecture, design, implementation, and best practices.
• Evaluate and incorporate emerging MLOps technologies to improve engineering velocity, scalability, and performance.
• Drive cross-functional alignment by partnering with Product Managers, TPMs, and engineering leaders to define and deliver the roadmap.
• Lead design reviews and establish technical standards to ensure high reliability, maintainability, and scalability across systems.
• Design and lead the development of DevOps processes and tooling necessary for scaling infrastructure and services.
• Foster a culture of innovation, technical excellence, and continuous improvement across the organization.

Requirements

• MS or PhD in Computer Science or a related field—or equivalent industry experience.
• 10+ years of experience building and operating production-scale systems.
• 3+ years of experience leading large-scale, GPU-intensive GenAI workloads (training, inference, and/or optimization).
• Proven track record of leading cross-functional teams on complex, high-stakes engineering initiatives.
• Exceptional communication and leadership skills, with a strong ability to drive alignment in matrixed environments.
• Deep experience in model serving, orchestration, and GPU resource management in large-scale deployments.
• Hands-on expertise with Kubernetes, distributed systems, and MLOps platforms.

Preferred Qualifications

• In-depth understanding of generative model architectures, including diffusion models, transformers, and GANs.