Job description

About Anyscale: At Anyscale anyscale.com, we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels. We’re commercializing Ray docs.ray.io, a popular open-source project that's creating an ecosystem of libraries for scalable machine learning. Companies like OpenAI thenewstack.io, Uber uber.com, Spotify engineering.atspotify.com, Instacart youtube.com, Cruise youtube.com, 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 wsj.com with $250+ million raised to date. About Ray Data Team: Ray Data docs.ray.io is a Python-native data processing engine and a one-stop shop for all AI data processing needs. Ray Data provides performant, first-class integration with cutting-edge AI frameworks using both multimodal and structured data. The Ray Data team develops and maintains Ray Data docs.ray.io, 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 anyscale.com. As part of this role, you will: - Design, build, and improve the core systems that power Ray Data docs.ray.io, 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 drive.google.com and the Right to Work posters in English and Spanish drive.google.com

Requirements

Must-haves

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.
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

Nice-to-haves

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Application prep

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Keywords to mirror

machine learningaimlpythonazure

Platform: ashby
Effort: quick

Benefits

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Dental

Vision

Pto

Parental Leave