Software Engineer, Ray Data

About the role

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.

A snapshot of projects you will work on

• 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

As part of this role, you will

• 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

We'd love to hear from you if have

• 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)