About The Position

As a Research Engineer/Scientist (Reinforcement Learning) at Percepta, you will work at the intersection of RL research and real-world deployment. You will advance the frontier of capabilities through research on decision-making for critical industries. You will collaborate closely with our Embedded Product Managers (EPMs) and engineers to ensure that our solutions transform how companies operate.

Requirements

  • Have an MS/PhD in Computer Science, ML, or related field, or equivalent experience.
  • Have a track record of effective RL work.
  • Are motivated by impact in critical industries including healthcare, supply chains, energy, and finance.
  • Understand how to perform rigorous RL experimentation.
  • Enjoy extreme ownership.
  • Believe that AI can drive transformative change in critical industries.

Nice To Haves

  • High performance, large scale distributed systems.
  • Large scale LLM training or RL training.
  • Possess strong programming skills, especially in Python.
  • Implementing LLM post-training algorithms.
  • Experience with vLLM/SGLang, Ray, Kubernetes (or AWS EKS).
  • Experience with distributed checkpointing, multi-node, multi-gpu training, custom KV-caching.
  • Experience with asynchronous training and inference, either with VeRL, ROLL, SkyRL, AReal, or with RL libraries like CleanRL.

Responsibilities

  • Identifying which real-world challenges are tractable for RL-guided decision making.
  • Develop RL methods to perform complex tasks in domains like planning, decision-making, or optimization.
  • Develop and maintain the experimental infrastructure that powers our research, from simulation environments and data pipelines to training and evaluation frameworks.
  • Conduct in-the-wild evaluations at scale that drive millions of dollars in value.
  • Partner with our applied AI engineers to transition successful research ideas into robust features of our Mosaic platform.
  • Communicate research outcomes to both technical and non-technical stakeholders, making sure everyone understands the 'so what' of research and how to apply it.
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