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# Research Engineer / Scientist - Reinforcement Learning (RL)

- **Company:** Perceptive missions LLC
- **Location:** Boston, United States
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Programming, Distributed Systems, Python (Programming Language), Node.Js, Large Language Models, Kubernetes, Information Technology, Data Pipelines, AWS EKS
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/research-engineer-scientist-reinforcement-learning-rl-percepta-8142430

## About the Role

* 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.

The following list can be a sign that you might be a good technical fit:

 * 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.

## Description

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., * 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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