Anyscale
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About the role: Ray aims to provide a universal API for building distributed applications (e.g.
At Anyscale https://www.anyscale.com/, we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels. We’re commercializing Ray https://docs.ray.io/en/latest/, a popular open-source project that's creating an ecosystem of libraries for scalable machine learning. Companies like OpenAI https://thenewstack.io/how-ray-a-distributed-ai-framework-helps-power-chatgpt/, Uber https://www.uber.com/blog/horovod-ray/, Spotify https://engineering.atspotify.com/2023/02/unleashing-ml-innovation-at-spotify-with-ray/, Instacart https://www.youtube.com/watch?v=3t26ucTy0Rs&list=PLzTswPQNepXmLUiL4F_1VHrPcCz1OeILw&index=23&pp=iAQB, Cruise https://www.youtube.com/watch?v=gj0BqvfX_wI&list=PLzTswPQNepXmLUiL4F_1VHrPcCz1OeILw&index=46&pp=iAQB, 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 https://www.wsj.com/articles/ai-startup-anyscale-adds-99-million-to-andressen-horowitz-led-funding-round-11661254200 with $250+ million raised to date.
Ray aims to provide a universal API for building distributed applications (e.g. a machine learning pipeline of feature engineering, model training, and evaluation). Data is usually a core element connecting these different stages, and therefore plays a critical role in Ray’s usability, performance, and stability. We are looking for strong engineers to build, optimize, and scale Ray’s Datasets https://docs.ray.io/en/latest/data/dataset.html library and data processing capabilities in general.
The Ray Data team currently develops and maintains the Ray Datasets https://docs.ray.io/en/latest/data/dataset.html library, which is already powering critical production use cases (e.g. large scale data compaction at Amazon https://www.anyscale.com/events/2022/01/20/ray-meetup-is-back-in-the-new-year-2022, and ML pipeline at Alibaba https://www.anyscale.com/blog/building-an-end-to-end-ml-pipeline-using-mars-and-xgboost-on-ray). Ray Datasets is a Python library built on top of Apache Arrow and Ray Core (Ray’s C++ backend), and the Ray Data team interacts closely with Ray Core components including the scheduler and the memory & I/O subsystems. The Ray Data team also works closely with Ray’s ML libraries including Train, RLlib, and Serve.
Performance of Ray Datasets https://docs.ray.io/en/latest/data/dataset.html at large scale (leveraging Arrow primitives, optimizing Ray object manager, etc.)
Integration with ML training and data sources
Stability and stress testing infrastructure
Lead future work integrating streaming workloads into Ray such as Beam on Ray
Differentiate Data operations in Anyscale hosted Ray service
Develop high quality open source software to simplify distributed programming (Ray)
Identify, implement, and evaluate architectural improvements to Ray core and Datasets
Improve the testing process for Ray to make releases as smooth as possible
Communicate your work to a broader audience through talks, tutorials, and blog posts
At least 5 years of relevant work experience
Solid background in algorithms, data structures, system design
Experience in building scalable and fault-tolerant distributed systems
Experience with data processing, database internals including Spark or Dask (streaming is a plus)
Anyscale Inc. is an Equal Opportunity Employer. Candidates are evaluated without regard to age, race, color, religion, sex, disability, national origin, sexual orientation, veteran status, or any other characteristic protected by federal or state law.
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