Anyscale에서 Ray Data 팀의 분산 시스템 엔지니어를 채용합니다. Python 기반의 오픈소스 프로젝트인 Ray Datasets를 고도화하고 대규모 데이터 처리 성능을 최적화하는 업무를 수행합니다. 분산 시스템 설계 경험과 데이터 처리 프레임워크(Spark, Dask 등)에 대한 깊은 이해가 필수적이며, 5년 이상의 경력을 요구합니다.
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 (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 library and data processing capabilities in general.
About the Ray Data team:
The Ray Data team currently develops and maintains the Ray Datasets library, which is already powering critical production use cases (e.g. large scale data compaction at Amazon, and ML pipeline at Alibaba). 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 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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