Sr Site Reliability Engineer, AI Platform Inference

About the position

We're looking for an outstanding, Site Reliability Engineer for Adobe's AI Inference Platform, Adobe Firefly. You will be part of a team of Site Reliability Engineers closely working with the Engineering teams on building, scaling, and securing the AI Platform. This enables the Firefly product teams to easily manage and deploy Machine Learning capabilities used by Adobe client applications. The Applied Research groups from Adobe Research and other App Teams in Adobe will deploy thousands of models onto this platform in a variety of lifecycle stages (early research, development, productization, optimization, etc). This platform will offer ML model serving at scale, with high-cost efficiency, and on a wide variety of hardware platforms across multiple clouds.

Responsibilities

• Identify and implement methodologies and solutions to increase reliability, scalability, security, and efficiency.
• Ensure the highest uptime and Quality of Service (QoS) for Adobe's customers through operational excellence.
• Define service level objectives (SLOs) and indicators (SLIs) to represent and measure service quality.
• Support and maintain globally distributed, multi-cloud (public and/or private) environments.
• Automate common, repeatable tasks at a large scale to streamline operational procedures.
• Identify areas to improve service resiliency through techniques such as chaos engineering, performance/load testing, etc.
• Coordinate with other Adobe platform teams and service providers (primarily AWS) to innovate on Generative AI as a Service.

Requirements

• A Bachelor's or Master's degree in Computer Science, Electrical Engineering, a related field, or equivalent industry experience.
• You excel in undefined environments and get excited about finding pragmatic solutions to complex technical or organizational challenges.
• You keep up with the industry trends and grow your knowledge and skills to solve technical problems.
• Experience in building and scaling distributed systems, as well as experience with containerization and orchestration technologies like Kubernetes.
• Production level expertise with containerization orchestration engines (e.g. Kubernetes) and proven understanding of modern, continuous development techniques and pipelines (IaC, CI/CD, ArgoCD, Git).
• Fundamental programming skills, ideally practical experience in one (and preferably more) of the following languages: Python, Go.
• Good knowledge of infrastructure configuration management tools like Ansible and Terraform.
• Experience in using observability and tracing-related tools like InfluxDB, Prometheus, and Elastic Stack.
• An understanding of AI/ML, including ML frameworks, public cloud, and commercial AI/ML solutions - familiarity with Pytorch, SageMaker, HuggingFace, NVIDIA TensorRT or OpenAI Triton a plus.

Benefits

• Compensation reflects the cost of labor across several U.S. geographic markets.
• Pay range for this position is $153,600 -- $286,600 annually.
• Eligible for long-term incentives in the form of a new hire equity award.