Principal Machine Learning Engineer

Adobe•San Jose, CA

About The Position

Firefly Foundry is Adobe's enterprise managed-service offering for custom multimedia generative AI — deep-tuned image, video, and 3D models built on each customer's IP, paired with creative production workflows and a media-intelligence layer, and deployed across new and existing Adobe surfaces and products, including Firefly, Photoshop, Illustrator, Express, Stock, and Premiere. We are hiring a Principal Machine Learning Engineer to serve as the technical lead for our GenAI Services area. This is not a model-training or research role — it is the senior-most hands-on engineering authority over how our generative models are architected, optimized , and served at enterprise scale. You will set the inference architecture and technical standards that a growing organization of engineers builds against, co-develop and optimize the inference code that makes those systems fast and cost-efficient, and architect the APIs and product backend that let Adobe's first-party and third-party models reach both internal applications and external plugin integrations. Where the Director owns the multi-year technical strategy, headcount, and company roadmap for the org, you own the architecture, technical depth, and hands-on execution that make that strategy real — spanning multiple engineering teams without owning their people management.

Requirements

MS or PhD in Computer Science, Machine Learning, or a related field — or equivalent industry experience.
8+ years of experience in machine learning engineering, including production-scale deployment and serving — not training or research experimentation.
3+ years leading the technical direction of large-scale, GPU-intensive GenAI inference systems — serving, architecture, and optimization.
Deep experience with inference frameworks and tools such as PyTorch , CUDA, Triton, TensorRT , Nvidia Dynamo, and Python.
Strong understanding of generative model architectures — diffusion models, transformers, GANs, LLMs — sufficient to make architecture and optimization calls and reason about output quality, in partnership with Applied Science.
Proven experience architecting multi-model pipelines and serving them behind APIs at enterprise scale.
Experience designing product backend systems and plugin architectures consumed by internal applications and external integrations.
Proven success leading cross-functional teams through complex, high-stakes technical initiatives, with a track record of driving alignment in matrixed organizations.
Excellent communication and technical leadership skills.

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.
Experience with RAG architectures and multi-turn, agentic conversational systems.
Experience with quantization, distillation, or other model-optimization techniques for inference.
Master's or PhD in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience building and leading production-scale ML systems.

Responsibilities

Lead the development of core GenAI services and APIs that integrate a wide range of first-party and third-party generative models into Adobe's flagship products.
Architect ML serving workflows for enterprise-scale model customization, deployment, and ecosystem integration — including externalizable, self-serve fine-tuning flows.
Co-develop and optimize GPU-accelerated inference pipelines — prioritizing latency, throughput, scalability, and reliability — using tools such as PyTorch , CUDA, Triton, and TensorRT .
Design and architect the product backend and plugin ecosystem that lets internal applications and external integrations consume Firefly Foundry's model services.
Provide hands-on technical leadership: guide engineers through architecture, design, implementation, and best practices, and mentor a growing organization of ML engineers.
Research and evaluate emerging inference and MLOps technologies — serving runtimes, quantization , GPU scheduling — to improve engineering velocity and system performance.
Lead design reviews and set technical standards, ensuring high reliability and maintainability across systems.
Drive cross-functional alignment with Product Managers, TPMs, and engineering leaders to define and deliver on the roadmap.
Foster a culture of technical excellence and continuous improvement across the organization.