The Ray Data team develops and maintains Ray Data https://docs.ray.io/en/latest/data/data.html, building the underlying distributed data processing infrastructure that powers modern AI workloads. We are a team of engineers passionate about solving challenging problems in distributed systems, data processing, and performance at scale. We are looking for exceptional engineers to build, optimize, and scale Ray Data for increasingly complex AI workloads, including multimodal data processing and large-scale batch inference.
Learn more about how Ray Data enables scalable multimodal AI workloads in Architecting Multimodal Data Pipelines That Scale with Ray https://www.anyscale.com/blog/architecting-multimodal-data-pipelines-that-scale-with-ray.
As part of this role, you will
Design, build, and improve the core systems that power Ray Data https://docs.ray.io/en/latest/data/data.html, with a focus on performance, scalability, and reliability.
Design and optimize distributed execution across different stages of data pipelines in heterogeneous environments.
Build data loading and processing solutions for production training and inference workloads.
Solve challenging problems in distributed execution, scheduling, resource management, data partitioning, fault tolerance, and performance optimization.
Make system-level architectural decisions and reason through tradeoffs in areas such as resource allocation, execution models, batch vs. streaming workloads, and consistency and availability.
Work with customers and new-age AI-native companies to understand and solve challenges in scaling their AI workloads.
We'd love to hear from you if you have
6+ years of experience building production-grade software, infrastructure, or developer-facing systems, with strong Python engineering experience.
6+ years of experience personally owning core architectural decisions within a distributed data or compute engine, rather than primarily operating or using a platform someone else designed.
Deep experience with distributed systems internals, such as scheduling, fault tolerance, data partitioning, distributed execution, performance optimization, or database and query engine internals.
A track record of reasoning through system-level tradeoffs and defending architectural decisions, such as batch vs. streaming, static vs. dynamic resource allocation, or consistency vs. availability.
Passion for solving the unsolved problems in large-scale AI infrastructure and building systems that enable the next generation of AI applications.
WHY ANYSCALE
We're on a mission to make scalable computing effortless. Ray is the AI Compute Engine at the center of some of the world's most powerful AI platforms
Our tech is in production at companies like OpenAI, Uber, Spotify, Instacart, and Cruise
We're backed by Andreessen Horowitz, NEA, and Addition, with $250M+ raised to date
Recent partnerships with Azure, CoreWeave, and Google Cloud are putting AI-native compute directly into enterprise environments
Competitive salary and equity, plus health/dental/vision coverage (many plans up to 99% employer-covered)
We offer flexible time off, paid parental leave, and mental health support
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.
Anyscale Inc. is an E-Verify company and you may review the Notice of E-Verify Participation https://drive.google.com/file/d/1Kt2S6_k_SjxaEdGowH4rngVdg2ApAQV3/view?usp=sharing and the Right to Work posters in English and Spanish https://drive.google.com/file/d/1K3Nz72xgsU2hngnVUEu53wEeZjbAMbnZ/view?usp=sharing
