Director, ML Services Engineering

Adobe

Quick summary

Work type: On-site
Location: San Jose
Salary: $265,700–$384,675 / yr
Posted: 2 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $227k

This role $325k

$157k most similar roles pay here $409k

This role pays more than 96% of similar roles. Most pay $202,837–$250,437 — the shaded band above. At the midpoint, this role pays about $325k versus about $227k for comparable roles.

Based on 240 similar postings.

Employer

About Adobe

Adobe Inc. is a global software company known for creative and multimedia software products including Photoshop, Illustrator, Acrobat, and its cloud-based Creative Cloud and Document Cloud suites. Industry: Creative & Digital Experience Software

Adobe currently has 325 open roles on FindRole.

Listed pay typically runs $183,300–$265,350 across 325 roles with salary data.

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At a glance

TL;DR · Director, ML Services Engineering

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As a Director of ML Engineering at Firefly Foundry, an enterprise managed-service offering for custom multimedia generative AI within Adobe, you will lead the engineering function responsible for productionizing and operating models for hundreds of enterprise customers. Your daily tasks include setting technical strategies to scale model services efficiently while ensuring cost optimization and high-quality service delivery. You will work closely with Applied Science teams to ensure inference quality matches training outcomes and collaborate with Firefly Foundry Studio to translate creative visions into reliable ML systems. Key skills required are extensive experience in applied machine learning, leading engineering organizations, and a deep understanding of generative model landscapes. Familiarity with high-resolution media pipelines is beneficial.

Skills

Python TensorFlow PyTorch Kubernetes AWS Azure GCP Docker CI/CD Prometheus Grafana PostgreSQL Redis GitLab Jenkins Scikit-learn CUDA NCCL Diffusion_models Transformers VAEs

What you'll do

Define and implement technical strategy for productionizing custom generative models.
Own unit economics of inference services, focusing on cost-to-serve and GPU utilization.
Design tenancy architecture to ensure data isolation and compliance with IP contracts.
Drive self-service roadmap expansion for broader customer reach and value.
Represent engineering in high-level discussions with enterprise customers and partners.

What we're looking for

10+ years of experience in applied machine learning and ML systems.
At least 5 years of leadership in engineering organizations, including managing managers.
Proven success in shipping generative AI products at enterprise scale.
Deep understanding of modern generative model architectures and techniques.
Strong intuition for large-scale inference economics and engineering realities.
Experience designing and operating end-to-end ML systems at production scale.

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