Software Engineer - Reinforcement Learning

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 the Role:RLlib is an open-source library for reinforcement learning that offers both high scalability and a unified API for a variety of applications. It is used by many high profile gaming and finance companies to build cutting-edge RL based systems. We're looking for software engineers with existing machine learning experience that are interested in continuing to enhance RLlib. We are looking for senior hires as well as less experienced but motivated individuals.

We'd love to hear from you if you have:

• Have relevant experience using RLlib or developing RL algorithms
• Have strong proficiency in Python and PyTorch or Tensorflow.
• Are excited about working with customers who are applying RL to their own use cases
• Are excited about working on open source and broadcasting new features to the community

As part of this role you will:

• Develop high quality open source software to simplify reinforcement learning (RLlib)
• Identify, implement, and evaluate promising new RL algorithms from the literature
• Improve the testing process for RLlib to make releases as smooth as possible
• Communicate your work to a broader audience through talks, tutorials, and blog posts

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. 
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