Ofertas › Anyscale
Anyscale · San Francisco · Remoto · $240K – $270K • Offers Equity
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.
WHAT YOU'LL DO
- Own a major technical area of Ray Core end to end, defining the roadmap, identifying the most important technical problems, and driving execution through production.
- Lead large, technically complex projects spanning multiple engineers, teams, and/or organizations.
- Set technical direction and make architectural decisions for distributed computing infrastructure used by demanding production workloads.
- Design, build, and evolve core distributed-systems primitives rather than simply integrating existing platforms or frameworks.
- Work on problems involving areas such as distributed execution, scheduling, resource management, fault tolerance, concurrency, networking, storage, or system performance.
- Stay hands-on with implementation and debugging in a systems-oriented codebase, while raising the technical bar for the engineers around you.
- Help shape the longer-term architecture and evolution of Ray Core as our workloads and scale continue to grow.