About Abundant

Abundant builds reinforcement learning environments for frontier AI labs. We design and operate the simulation infrastructure where next-generation models learn to reason, act, and solve complex problems. We’re a small, high-impact team scaling fast.

The Role

Research PM (RPM) is an emerging role that we are pioneering at Abundant. RPMs are PMs of the model, unlike traditional PMs who own an application or a feature. RPMs design and implement model capabilities, either as part of an AI lab, data lab, or other research organization. 

Like traditional PMs, RPMs are still the voice of the user. RPMs talk to users or enterprises to find out how their work is performed and how they use AI. Then they decide how to distill the users’ feedback into evaluation and training data. We are RPMs that are triple-threats: folks that read up on the latest machine learning literature, and can and own technical tooling, and can scale up teams of hundreds. For folks in the AI data industry, you’ll recognize that this is the evolution of the SPL role from an purely-operations job into an all-rounder that includes research, product and execution.

An RPM will end-to-end execution of Abundant’s most critical projects. You will translate ambiguous research requirements into structured workflows, manage distributed expert teams, and deliver flawless results under tight timelines. This is a founding-level operations role with outsized ownership.

What You’ll Do

• Own project delivery end-to-end — scoping, design, contributor ops, QA, and handoff to AI lab partners
• Build and manage relationships with researchers at frontier labs
• Recruit, scale, and run distributed teams of domain experts — sourcing, performance, quality, engagement
• Build the operational playbook — processes, tooling, and QA systems that don’t exist yet

Who you are

• Relentless executor — you have extreme agency and drive; when you see a problem you fix it, when a path doesn’t exist you create one
• Thrive in chaos — ambiguity energizes you; you’ve built systems and processes from nothing in fast-moving environments
• High-autonomy track record — experience in operations, consulting, or program management where you owned outcomes, not just tasks
• Technically fluent — you understand software systems and data pipelines well enough to earn credibility with engineers

Nice to Have

• Familiarity with AI evaluation, benchmarks, or reinforcement learning

Requirements

Deep experience in one or more of the following:

• ML engineering or research
• Reinforcement learning and/or agentic harnesses
• Agent evaluation and benchmarking
• High-scale and high-reliability batch data pipelines