About Anyscale:
At Anyscale, we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels. We’re commercializing Ray, a popular open-source project that's creating an ecosystem of libraries for scalable machine learning. Companies like OpenAI, Uber, Spotify, Instacart, Cruise, 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 with $250+ million raised to date.
About Ray Data Team:
Ray Data 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, 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.
As part of this role, you will:
Design, build, and improve the core systems that power Ray Data, 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
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 and the Right to Work posters in English and Spanish
Skills Required
- 6+ years of experience building production-grade software, infrastructure, or developer-facing systems
- Strong Python engineering experience
- 6+ years personally owning core architectural decisions within a distributed data or compute engine
- Deep experience with distributed systems internals, including scheduling, fault tolerance, data partitioning, distributed execution, performance optimization, or database and query engine internals
- Experience reasoning through system-level tradeoffs and defending architectural decisions
- Passion for solving large-scale AI infrastructure problems
Anyscale Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Anyscale and has not been reviewed or approved by Anyscale.
-
Fair & Transparent Compensation — Pay is considered market-based with target ranges shown in postings and a stated market-based philosophy. Feedback suggests this clarity and consistency aid confidence in pay fairness.
-
Equity Value & Accessibility — Equity is commonly included in offers and is positioned as a meaningful part of total compensation for many roles. Feedback suggests this equity participation enhances perceived overall pay competitiveness.
-
Healthcare Strength — Health, dental, and vision coverage are described as robust with many plan options, alongside mental-health support and fertility benefits. Feedback suggests this strong core healthcare offering increases perceived benefits quality.
Anyscale Insights
What We Do
Distributed computing made simple Anyscale enables developers of all skill levels to easily build applications that run at any scale, from a laptop to a data center.






