Powered by Ray, Anyscale helps AI builders run data-intensive workloads to build and deploy Foundation Models and AI at scale on any cloud. Backed by NEA, a16z and Amplify.
About the role
Ray aims to provide a universal API for building distributed applications. To achieve this goal requires a distributed system with high levels of performance and reliability. We're looking for engineers with systems software experience that are interested in contributing to the Ray backend.
What they're looking for
- At least 5 year of relevant work experience
- Experience in building scalable and fault-tolerant distributed systems
- Extensive experience working in C/C++ and on low level operating systems
- Solid background in algorithms, data structures, system design
- Knowledge of distributed model training and inference (e.g. tensor parallel, pipeline parallel) is preferred
- Knowledge of GPU programming is preferred
More about this role
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.
Ray aims to provide a universal API for building distributed applications. To achieve this goal requires a distributed system with high levels of performance and reliability. We're looking for engineers with systems software experience that are interested in contributing to the Ray backend.
The Ray Core team develops and maintains the Ray C++ backend (e.g., distributed scheduler, language runtime integration, I/O and memory subsystems). We are...
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