Site Reliability Engineer, Platform Infrastructure (Foundations)

About Anyscale:

At Anyscale, we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels. We’re commercializing Ray, a popular open-source project that's creating an ecosystem of libraries for scalable machine learning. Companies like OpenAI, Uber, Spotify, Instacart, Cruise, and many more, have Ray in their tech stacks to accelerate the progress of AI applications out into the real world.

With Anyscale, we’re building the best place to run Ray, so that any developer or data scientist can scale an ML application from their laptop to the cluster without needing to be a distributed systems expert.

Proud to be backed by Andreessen Horowitz, NEA, and Addition with $250+ million raised to date.

About the role:

Anyscale is looking for a Site Reliability Engineer to join the Infrastructure team. Anyscale aims to provide the next generation of tools and infrastructure to make developing and running distributed AI applications in the cloud as easy as on your laptop. As part of the Infra team, we build the scalable, secure, and robust backbone that enables this vision.

Our team is responsible for both the control plane, which orchestrates cluster management, scheduling, and user access, and the data plane, which ensures high-performance execution of distributed workloads.

We are seeking a talented engineers with a strong background in control plane and data plane development, along with expertise in Kubernetes, container orchestration, and cloud-native infrastructure. You will play a crucial role in designing, implementing, and optimizing the critical infrastructure that powers Anyscale’s cloud platform.

You will have the opportunity to work on open-source Ray, contribute to our infinite laptop proprietary product, and develop seamless integration between the two, while also delivering high-impact features for our customers.

A snapshot of projects you may work on

• Design, build, and scale services that orchestrate Ray clusters across cloud and on-prem environments, supporting both VM-based and Kubernetes-based deployments

• Optimize control plane components for large-scale, distributed AI/ML workloads

• Build intelligent scheduling and resource management systems for heterogeneous compute clusters

• Develop features to enhance the reliability, performance, scalability, and observability of Anyscale-managed Ray workloads

• Support and optimize accelerator integration (e.g., GPUs, TPUs).

• Handle container image management and dependency resolution for distributed workloads

• Participate in code reviews, design and architecture discussions