Anyscale Engineering Manager to lead the open source Ray Data team at Anyscale, focusing on scalable data processing for AI workloads. Responsible for technical direction, team management, and community engagement within the Ray ecosystem.
Responsibilities
Anyscale is looking for an engineering leader to manage the open source Ray Data team. Ray Data is the Python-native data processing engine for AI workloads on Ray. It handles multimodal and structured data and powers batch inference, data loading for distributed training, and large-scale preprocessing. The team owns the open source library end to end, including:
The streaming execution engine, including scheduling, backpressure, and resource management across CPUs and GPUs
Batch inference for LLMs and multimodal models, including integrations with inference engines such as vLLM
Data loading for production training workloads with Ray Train and popular ML frameworks
Connectors for data sources and formats such as Parquet, Iceberg, Delta Lake, Lance, and cloud object storage
Performance, stability, and fault tolerance at large scale
This is a first-line engineering management role where you'll lead a team of engineers, stay involved in technical design and code, and own the team's roadmap within the open source project. Ray Data has a large and active community, so you'll also work closely with external contributors to review designs, guide technical direction, and support the project's continued growth and success. The team plays an important role in the broader Ray ecosystem and contributes directly to Anyscale's success.
Set the technical direction and roadmap for open source Ray Data, balancing community needs, customer needs, and Anyscale's product goals
Drive execution of the team's key projects, from design review through release
Hire, coach, and grow a world-class team of distributed systems and data infrastructure engineers
Run the team day to day: planning, prioritization, project tracking, on-call, and release quality
Stay technically hands-on through design and code review, performance investigations, and escalations on critical production issues
Qualification
Hands-on experience with RayExperience with Apache ArrowExperience with GPU workloads
Preferred
Experience leading or maintaining a widely used open source project, including working with external contributors and project governance
Hands-on experience with Ray, Spark, Flink, Beam or similar engines
Experience with Apache Arrow, columnar formats, or table formats such as Iceberg and Delta Lake
Experience with GPU workloads, LLM or multimodal batch inference, or data loading for large-scale model training
Experience operating data or ML infrastructure in production
