Software Engineer, Ray Data
Anyscale · Posted Oct 29
Distributed computing platform and managed cloud services for scaling artificial intelligence and machine learning workloads
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About the role
Anyscale commercializes Ray, an open-source distributed computing project creating an ecosystem of libraries for scalable machine learning, and builds a hosted platform to run Ray. The Ray Data team develops and maintains the Ray Datasets library, a Python library built on Apache Arrow and Ray Core (Ray's C++ backend) that powers large-scale production data and ML pipeline use cases. This role involves building, optimizing, and scaling Ray's Datasets library and data processing capabilities, improving performance at scale, testing infrastructure, and integration with ML training and data sources.
What you will do
- 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
Skills used in this role
What the employer is looking for
- 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)
Benefits and support
- Compensation and benefits are detailed in the job posting
About Anyscale
Anyscale is the company behind Ray, an open-source distributed computing framework designed to scale artificial intelligence and machine learning workloads seamlessly from a laptop to the cloud. Its managed platform offers elastic GPU orchestration, distributed training, and batch inference capabilities to help development teams build production-grade AI applications without infrastructure complexities.
- Industry
- Artificial Intelligence
- Company size
- 200-500 employees
- Founded
- 2019
- Location
- San Francisco, California, USA
- Funding stage
- Series C
Funding
Series C · $281M raised
- 2021-10-15Series C$199M
- 2020-10-21Series B$40M
- 2019-08-02Series A$20M
- 2019-02-18Seed$22M
Leadership
Chief Executive Officer
Co-founder & Chief Strategy Officer
Co-founder & CTO
Co-founder & Executive Chairman
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