Staff ML Platform Engineer: Scale GPUs & Production

JobCubby; San Jose, Northern (CA, KY); Hybrid; USD 212,000 - 307,000; Full time

Job summary

Adobe’s Staff Machine Learning Platform Engineer will own core parts of the platform, focusing on maximizing GPU fleet utilization, scalable training, and low-latency inference.

You will set direction, partner with ML researchers and product teams, and mentor the platform organization as Adobe scales its AI initiatives.

This role demands deep distributed systems experience, Kubernetes expertise, and strong collaboration across multiple teams in a fast-moving environment.

Qualifications

• 7+ years building and operating large-scale platform, infrastructure, or distributed systems in production.
• Deep expertise in distributed systems and cloud infrastructure, including Kubernetes, containerized workloads, and operating large multi-node and multi-region clusters.
• Strong programming ability in Python and at least one systems language (Go, C++, Rust, or Java).
• A track record of designing systems that other engineers build on, making deliberate architectural tradeoffs and taking them from design to production at scale.
• A bias for measurable outcomes (latency, throughput, utilization, reliability) and the collaboration skills to drive them across teams and partners.

Responsibilities

• Own the architecture and roadmap for major components of the ML compute and inference platform, such as training orchestration, GPU scheduling and utilization, model serving, or the developer-facing surfaces ML teams build on.
• Design and operate distributed systems that run large-scale training and low-latency, high-throughput inference reliably across thousands of accelerators.
• Drive multi-tenancy, elasticity, and cost/utilization efficiency across a shared GPU fleet serving many teams with competing demands.
• Build the paths that move a model from experiment to production without re-implementation, from packaging and registry through deployment and safe rollout.
• Set engineering standards for reliability, observability, and performance, and raise the bar for how the platform is built and operated.
• Partner with ML researchers and product teams to turn emerging workloads into first-class platform capabilities, and inform capacity and hardware strategy.
• Provide technical leadership and mentorship across the platform organization.

Skills

Distributed systems

Kubernetes

Python

Go/C++/Rust/Java

Performance & scalability

Collaboration across teams

Tools

Kubernetes

Docker

PyTorch

TensorRT

Job description

Adobe’s Staff Machine Learning Platform Engineer will own core parts of the platform, focusing on maximizing GPU fleet utilization, scalable training, and low-latency inference.

You will set direction, partner with ML researchers and product teams, and mentor the platform organization as Adobe scales its AI initiatives.

This role demands deep distributed systems experience, Kubernetes expertise, and strong collaboration across multiple teams in a fast-moving environment.