Research Engineer Scientist – Reinforcement Learning (RL)
Share
Job Location:
New York City, NY - USA
Monthly Salary:
Not Disclosed
Posted on:
30+ days 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.
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 were 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 problems. This isnt a 9-5 nor is it a job were ever going to monitor your hours. We promise to put work in front of you that matters and in return we ask you to promise to care.
Win for the customer: Everyone is an engineer and the job of an engineer is to deliver outcomes not outputs. Everything we dothe products we build the partnerships we launch the strategy we setexists to make our customers successful. Delivery is the strategy.
Make the call: Organizations are only as strong as the pace at which they make decisions. Everyone at Percepta should feel empowered to commit and shape the ambiguity in front of them. But make the call cuts both ways: make the decision and make the phone call. High-agency decision-making only works with high-bandwidth communication and we commit to never operate in silos.
Intensity with kindness: We believe in excellence in execution candor in feedback ruthlessness in prioritization and survivalist urgency. We also believe you dont need to be an asshole to deliver on any of this. The trust built through shared kindness and vulnerability is what makes the intensity sustainable.
Required Experience:
IC
