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Software Engineer (Ray Data)

AnyScale

San Francisco, CA · $215k–$230k a year · Posted August 28, 2026

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About the job

About Anyscale:

At Anyscale https://www.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 https://docs.ray.io/en/latest/, a popular open-source project that's creating an ecosystem of libraries for scalable machine learning. Companies like OpenAI https://thenewstack.io/how-ray-a-distributed-ai-framework-helps-power-chatgpt/, Uber https://www.uber.com/blog/horovod-ray/, Spotify https://engineering.atspotify.com/2023/02/unleashing-ml-innovation-at-spotify-with-ray/, Instacart https://www.youtube.com/watch?v=3t26ucTy0Rs&list=PLzTswPQNepXmLUiL4F_1VHrPcCz1OeILw&index=23&pp=iAQB, Cruise https://www.youtube.com/watch?v=gj0BqvfX_wI&list=PLzTswPQNepXmLUiL4F_1VHrPcCz1OeILw&index=46&pp=iAQB, 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 https://www.wsj.com/articles/ai-startup-anyscale-adds-99-million-to-andressen-horowitz-led-funding-round-11661254200 with $250+ million raised to date.

About Ray Data Team:

Ray Data https://docs.ray.io/en/latest/data/data.html is Python-native data processing engine that is a one stop shop for all AI data processing needs. Ray Data provides performant, first-class integration with cutting edge AI frameworks using both multi-modal and structured data.

The Ray Data team currently develops and maintains Ray Data https://docs.ray.io/en/latest/data/data.html. We are a team of engineers passionate about building a Data processing engine which is a one-stop shop for all of your ML/AI needs. We are looking for exceptional engineers to build, optimize, and scale Ray for modern and increasingly complex AI workloads.

As part of this role, you will:

- Improve the performance of Ray Data https://docs.ray.io/en/latest/data/data.html and multi-modal batch inference use cases.

- Ensure efficient scaling across different stages of the Data pipeline in a heterogeneous environment.

- Building data loading solutions for production training workloads.

- Focus on stability and fault tolerance at high scale

- Working with customers and new age AI native companies in scaling their AI workloads.

We'd love to hear from you if have:

- At least 3-4 years of relevant work experience

- Solid background in building scalable and fault-tolerant distributed systems

- Experience with data processing, database internals.

- Passionate about large scale systems and performance for AI.

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