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.
Anyscale is looking for a strong Product Manager to lead the Ray Core (Core Runtime) team, which includes both the open source Ray distributed computing framework, and proprietary features. We are looking for candidates with experience working with a high performance engineering team. As an open source project, Ray is about making general purpose distributed computing easy, and Ray Core is the fundamental architecture behind this “infinite laptop” mission.
The Ray Core team is responsible for developing this core distributed computing framework (with basic abstractions of Tasks, Actors, Objects) to support Ray developers, and other Ray libraries (e.g. Datasets, ML libraries). The work also includes adding proprietary features to make the Anyscale SaaS offering more competitive.
You can learn more about Ray Core with these materials:
- Our research papers on NSDI ‘21 and HotOS ‘21 describing the architectural vision of a Distributed Futures system.
In this position, you will guide the vision, strategy and execution with a high-performing team. This team is critical to everything that Anyscale and Ray achieve and will play a pivotal role in the success of the company.
Anyscale is looking for a strong Product Manager to lead the Ray Core (Core Runtime) team, which includes both the open source Ray distributed computing framework, and proprietary features. We are looking for candidates with experience working with a high performance engineering team. As an open source project, Ray is about making general purpose distributed computing easy, and Ray Core is the fundamental architecture behind this “infinite laptop” mission.
The Ray Core team is responsible for developing this core distributed computing framework (with basic abstractions of Tasks, Actors, Objects) to support Ray developers, and other Ray libraries (e.g. Datasets, ML libraries). The work also includes adding proprietary features to make the Anyscale SaaS offering more competitive.
You can learn more about Ray Core with these materials:
- Our research papers on NSDI ‘21 and HotOS ‘21 describing the architectural vision of a Distributed Futures system.
In this position, you will guide the vision, strategy and execution with a high-performing team. This team is critical to everything that Anyscale and Ray achieve and will play a pivotal role in the success of the company.
What You'll Do:
What You'll Need:
Compensation
This role is also eligible to participate in Anyscale's Equity and Benefits offerings, including the following:
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
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.