Senior GenAI Inference Architect

Adobe; San Francisco (CA); On-site; USD 262,000 - 379,000; Full time

Job summary

Adobe seeks a Principal Machine Learning Engineer to be the technical lead for GenAI Services. You will own architecture, optimization, and serving of heterogeneous generative models at enterprise scale across Adobe products and plugins.

You will guide multiple teams, set standards, and mentor engineers while evaluating new inference and MLOps technologies to accelerate delivery and reliability.

Qualifications

• 8+ years of ML engineering with production deployment.
• 3+ years leading technical direction of large‑scale GenAI inference systems.
• Experience with inference frameworks and tools such as PyTorch, CUDA, Triton, TensorRT.
• Strong understanding of diffusion models, transformers, LLMs and multi‑model architectures.

Responsibilities

• Lead development of core GenAI services and APIs for integrating models into Adobe products.
• Architect ML serving workflows for enterprise deployment and fine‑tuning flows.
• Co‑develop and optimize GPU‑accelerated inference pipelines prioritizing latency and throughput.
• Design product backend and plugin ecosystem for internal apps and external integrations.
• Provide hands‑on technical leadership and mentorship to ML engineers.
• Research and evaluate emerging inference and MLOps technologies to boost velocity and performance.
• Lead design reviews and set technical standards across teams.

Skills

GenAI inference

GPU‑accelerated inference

PyTorch

CUDA

Triton

TensorRT

Python

Leadership

API design

Education

MS or PhD in CS/ML

Tools

Kubernetes

MLOps platforms

Job description

Adobe seeks a Principal Machine Learning Engineer to be the technical lead for GenAI Services. You will own architecture, optimization, and serving of heterogeneous generative models at enterprise scale across Adobe products and plugins.

You will guide multiple teams, set standards, and mentor engineers while evaluating new inference and MLOps technologies to accelerate delivery and reliability.