Research Engineer / Scientist – Reinforcement Learning (RL)

Research Engineer / Scientist – Reinforcement Learning (RL) WHO WE ARE Percepta’s mission is to transform critical institutions with applied AI. We care that industries that power the world (e.g. healthcare, manufacturing, energy) benefit from frontier technology. To make that happen, we embed with industry-leading customers to drive AI transformation. We bring together: - Forward-deployed expertise in engineering, product, and research - Mosaic, our in-house toolkit for rapidly deploying agentic workflows - Strategic partnerships with Anthropic, McKinsey, AWS, companies within the General Catalyst portfolio, and more Our team is a quickly growing group of Applied AI Engineers, Embedded Product Managers and Researchers motivated by diffusing the promise of AI into improvements we can feel in our day to day lives. Percepta is a direct partnership with General Catalyst, a global transformation and investment company. ABOUT THE ROLE 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. ROLE AND 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. INDICATORS OF A GOOD FIT - 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 https://github.com/volcengine/verl/tree/main, ROLL https://github.com/alibaba/ROLL, SkyRL https://github.com/NovaSky-AI/SkyRL, AReal https://github.com/inclusionAI/AReaL, or with RL libraries like CleanRL https://github.com/vwxyzjn/cleanrl. We're working against an incredibly ambitious mission. It won't be easy but it will likely be the most fulfilling work of your career. If that excites you, let's chat, even if you don't meet all of the qualifications above. OUR VALUES Dream bigger: We have the unique privilege of taking on the most ambitious problems and we should chase them with optimism, responsibility, and genuine belief that we can make it happen. We have to embrace the hard things when no one else will. Heart in the game: What we're doing matters and we have to give a shit. Internally, that means fixing badness when you find it. Externally, it means honoring the trust our customers place in us with their most important ... (truncated, view full listing at source)