Environments and datasets for RL. Powering the world. Backed by Y Combinator.
About the role
You will own a research agenda where research meets production, co-designing strategies with researchers from frontier AI labs. You are the “PM of the model”: you pick the problem, design the data and reward signal that moves it, and see it through to a live training run. The job rewards a founder mentality and extreme ownership, including going into unfamiliar domains, from chemical engineering to complex legal logic, and getting to ground truth fast on problems others have written off.
What they're looking for
- You write a lot of code. Strong software engineering and deep Python, comfortable owning a system end to end and debugging through layers you did not write
- You have built evaluations, RL environments, agent harnesses, verifiers, or execution sandboxes, and you have opinions about which of them can be trusted
- You have shipped research into production, particularly post-training, distillation, and high-stakes evaluation
- You have worked on large-scale agent systems: orchestration frameworks, tool APIs, distributed execution, observability, and logging
- You get to ground truth in an unfamiliar domain by building something and reading outputs, not by reading about it
- You notice the misspecified task, the ambiguous prompt, and the grader that can be gamed
More about this role
Abundant is an applied research lab focused on scaling reinforcement learning for safe and reliable agentic capabilities. We are an extremely talent-dense team of researchers, roboticists, founders, and operators whose work includes the Waymo Driver.
You will own a research agenda where research meets production, co-designing strategies with researchers from frontier AI labs. You are the “PM of the model”: you pick the problem, design the data and reward signal that moves it, and see it through to a live training run. The job rewards a founder mentality and extreme ownership, including going into unfamiliar domains, from chemical engineering to complex legal logic, and getting to ground truth fast on problems others have written off.
Foundational capability research and execution. Architect and execute a research agenda to find simple, generalizable ideas that advance model reasoning at scale, and own it from hypothesis through to running in live systems.
Design tasks and verifiers that stay hard. Build long-horizon agentic tasks that are still difficult a model generation from now, with graders where partial credit means something and the obvious exploit does not score.
Diagnose...
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