Job Description

Our Company



Changing the world through digital experiences is what Adobe's all about. We give everyone--from emerging artists to global brands--everything they need to design and deliver exceptional digital experiences! We're passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen.



We're on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours!



Role Overview

We are looking for a Site Reliability Engineer with a strong developer mindset and 4-5 years of experience to help us build and operate scalable, reliable systems. This is not a "just keep the lights on" SRE role-- you'll be writing production-grade code , building tools, automating platforms, and contributing directly to both infrastructure and developer productivity.

The ideal candidate brings together SRE discipline, deep networking knowledge, Kubernetes expertise , and solid software engineering skills , with an understanding of GPU-based workloads and AI/ML inference/training pipelines .

About the Platform

You'll be working on a cutting-edge platform designed to train and serve large-scale machine learning models . The platform supports everything from small-scale experimentation to massive, distributed training jobs running on GPU clusters . It provides developers and ML engineers with the tools to quickly onboard, monitor, and scale their workloads--whether it's a lightweight prototype or a production-grade deep learning model powering real-world applications.

Key platform features include:

Dynamic GPU orchestration using Kubernetes

Built-in support for training and inference workflows

End-to-end observability and cost tracking

High developer velocity via self-service tooling

Your contributions will directly impact the reliability, scalability, and efficiency of this platform and enable AI teams to innovate faster.

What You'll Do

Build tools, APIs, and platforms to improve reliability, deployment, and performance.

Architect and scale Kubernetes-based infrastructure for high-performance workloads (including GPU).

Write clean, efficient, and maintainable code in Python, Go, or similar languages.

Own and evolve CI/CD pipelines, infrastructure-as-code systems (Terraform, Helm), and service observability.

Troubleshoot and resolve complex system, network, and application-level issues.

Participate in blameless incident response, root cause analysis, and reliability reviews.

What You Bring

Core Requirements:

4-5 years of experience in SRE, DevOps, or platform engineering roles.

Strong development background with fluency in Python, Go, or similar languages .

Solid understanding of Kubernetes internals , workload orchestration, and Helm.

Deep knowledge of networking fundamentals :

DNS, TCP/IP, routing, VPNs, firewalls.

Experience with infrastructure automation and configuration management.

Understanding of GPU scheduling, resource allocation , and NVIDIA ecosystem tools.

Familiarity with service mesh, observability stacks (Prometheus, Grafana, OpenTelemetry), and cloud-native patterns.

Bonus Points:

Experience supporting AI/ML pipelines , especially GPU-based training/inference.

Contributions to open-source projects or internal developer platforms.

Why This Role

You'll write software, not just YAML.

You'll get to work on real AI infrastructure challenges (not just buzzwords).

You'll have impact across developer productivity, platform scalability , and service reliability .

You'll join a team that values code quality, systems thinking, and ownership .



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