Research Engineer Scientist – Reinforcement Learning (RL)
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Job Location:
New York City, NY - USA
Monthly Salary:
Not Disclosed
Posted on:
6 hours ago
Vacancies:
1 Vacancy
Job Summary
Who we are
Perceptas 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 ROLL SkyRL AReal or with RL libraries like CleanRL.
Were working against an incredibly ambitious mission. It wont be easy but it will likely be the most fulfilling work of your career. If that excites you lets chat even if you dont meet all of the qualifications above.
Required Experience:
IC
Key Skills
- Laboratory Experience
- Machine Learning
- Python
- AI
- Bioinformatics
- C/C++
- R
- Biochemistry
- Research Experience
- Natural Language Processing
- Deep Learning
- Molecular Biology
