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Partner with strategic customers to lead on-site proof-of-value engagements, deploy and drive enterprise adoption of Ray and the Anyscale platform, build demos and reference architectures, advise stakeholders, accelerate deals, and relay customer feedback to product and engineering.

As a Software Engineer at Anyscale, you'll build AI serving infrastructure, focusing on distributed systems, and ensuring high performance and reliability for ML applications.

The Software Engineer will develop and optimize infrastructure for distributed AI workloads, focusing on cloud-native deployments and systems orchestration while enhancing reliability, performance, and scalability.

The role focuses on optimizing ML inference at scale, collaborating with product teams, integrating Ray Data, and enhancing open-source contributions.

Develop high-quality open-source software to simplify distributed programming while working on architectural improvements and enhancing testing processes for the Ray core.

Develop high-quality software for distributed programming, optimize Ray's Datasets library, and improve testing processes. Communicate work through tutorials and talks.

Design and implement scalable cloud infrastructure for distributed AI applications, focusing on control and data planes, optimizing performance and reliability of services, and integrating with existing tools.

Design, build, and scale control- and data-plane infrastructure for distributed AI workloads. Improve reliability, performance, scheduling, and observability for Ray clusters across cloud and on-prem environments. Support accelerator integration, container image management, and provide on-call troubleshooting and cross-team collaboration.

Lead and scale technical programs for Anyscale's core product, coordinating engineering and product teams to plan requirements, identify risks, manage schedules, and deliver commercial ML/AI solutions on cloud and big-data platforms.

Build and optimize Ray Data: improve performance and scaling for multi-modal batch inference, implement production data-loading solutions, ensure stability and fault tolerance at high scale, and collaborate with customers to scale AI workloads.