Senior ML Platform Engineer: Scalable GPU & Production ML

adobe; San Jose (CA); On-site; USD 183,000 - 265,000; Full time

Job summary

Adobe is seeking a Staff Machine Learning Platform Engineer to own core parts of the AI compute and inference platform. The role focuses on maximizing GPU fleet utilization, moving models from experiment to production, and serving inference with low latency at global scale.

You will set technical direction, define architecture, and collaborate with hundreds of engineers to enable training and deployment for AI workloads across Creative Cloud, Experience Cloud, and Firefly.

Qualifications

• 7+ years building and operating large-scale platform, infrastructure, or distributed systems in production.
• Deep expertise in distributed systems, cloud infrastructure, Kubernetes and multi-node clusters.
• Strong programming ability in Python and at least one systems language (Go, C++, Rust, or Java).

Responsibilities

• Own architecture and roadmap for major components of ML compute and inference platform.
• Design and operate distributed systems for large-scale training and low-latency inference.
• Drive multi-tenancy, elasticity, and cost/utilization efficiency across a shared GPU fleet.
• Move models from experiment to production without re-implementation; packaging, registry, deployment, and rollout.
• Set engineering standards for reliability, observability, and performance across the platform.
• Partner with ML researchers and product teams to turn workloads into platform capabilities.
• Provide technical leadership and mentorship across the platform organization.

Skills

Distributed systems

Kubernetes

Python

Go

C++

Rust

Java

Architecture design

Observability

Performance

Job description

Adobe is seeking a Staff Machine Learning Platform Engineer to own core parts of the AI compute and inference platform. The role focuses on maximizing GPU fleet utilization, moving models from experiment to production, and serving inference with low latency at global scale.

You will set technical direction, define architecture, and collaborate with hundreds of engineers to enable training and deployment for AI workloads across Creative Cloud, Experience Cloud, and Firefly.