Environments and datasets for RL. Powering the world. Backed by Y Combinator.
About the role
As a Member of Technical Staff, you will work directly with Abundant's founding team to architect the next generation of model reasoning and intelligence. You will lead at the absolute frontier where research and execution collide, co-designing strategies alongside top-tier researchers from AI labs to push SOTA boundaries. This is a role for a extremely strong engineers who are already contributing frontier research.
What they're looking for
- Proven experience shipping research directly to production and live systems, specifically focusing on advanced post-training, distillation, and high-stakes evaluation methodologies
- Large-scale agent systems: orchestration frameworks, tool APIs, distributed execution, observability, and logging infrastructure
More about this role
IMPORTANT: in the application form, please specify what term you would like to begin your internship (winter/summer/fall)
AI models rely on two fundamental ingredients: compute and data. Abundant is building the NVIDIA of training data. NVIDIA, the leader in compute, has a peak market cap of $5T and generated $130B in revenue last year as the need for scaling compute has exploded. We believe the need to scale data is just beginning, as we move beyond SFT and human supervision to RL and Learning from Experience.
Our founding team consists of former founders, ML engineers, roboticists and data leads from Waymo, Google, Mercor and AWS. Our team has previously worked with DeepMind to deploy deep learning models at 1B user scale, trained SOTA models for self-driving at Waymo, and scaled data pipelines of tens of thousands of human annotators at YouTube. Our pioneering work in human computation, synthetic data, simulation and RL give us the advantage in delivering results to our customers.
Why now? Training data is more important and more scarce than ever before. Scaling laws dictate that linear improvement in model performance demands an exponential increase in training data. But there...
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