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 is only one World Wide Web and most of it has already been trained on. The next advances will require major advances in simulation, synthetic data and learning from experience.
What happens if we succeed? Abundant will be the core enabler for not only AGI, but ASI and physical intelligence. Most of the challenges in model algorithms and compute are already solved. What’s missing? The data necessary to move from general knowledge to domain expertise; from chatbots to agents; and from text to multimodal and physical AI. Ask any AI researcher or roboticist: the core bottleneck to progress is the availability of data, hence “_abundant data_”.
Abundant works with a majority of the top AI labs, as well as frontier startups and F500 enterprises.
As a Software Engineering Intern (Research Focused), you will work closely with our engineering team and founders to support the development of customer-facing products and internal research tooling. This role is designated as a general research internship, focusing exclusively on research, evaluation, and benchmark design.
You will assist in tasks related to the Core Platform, including core simulation engines, data creation tooling, and experimentation platforms. Your primary focus will be on benchmarking and evaluation tasks to help the team maintain high data quality standards.
In this role, you will spend the majority of your time coding and executing experiments under the guidance of a mentor. You will contribute to improving simulation performance and help transition core features into more modular components while learning how to handle large-scale event processing.
We are looking for students or recent graduates with experience or strong interest in one or more of the following:
We’re looking for folks that are obsessive about their work. In data, quantity is important but data quality is the differentiator for the winner in the space. In addition to this mindset, here are some skills that are a pre-requisite for working in startups:
Here’s what you’re signing up for when you interview with us.
The Craftsman
The Craftsman cares about their work, simply for the art of it. They may put extra care into UX or design, or into data quality, or customer success. The Craftsman is energized by putting out great work.
The Underdog
The Underdog is dying to prove themselves. They’ve overlooked; they haven’t challenged by their school or company, or they are tired of politics at a FAANG company. They’re looking for a chance to maximize their full potential and talent.
The Antifragilist
Our team collectively has an uncommon trait: high pain tolerance. The Antifragilist is not simply stoic in the face of challenge. They are like an immune system: they grow and become stronger in the face of each challenge.
Agent simulation and RL for researchers
Salary
$8,000 - $12,000
Location
San Francisco, CA, US
Last stage
Pre-seed
Investors
No applications, no recruiter spam. Just the intro.
A few questions to make sure this role is the right shape for you. Two minutes.
I write the intro, send it to the founder, and handle the back-and-forth.
Meji Abidoye
LinkedInIf they’re a yes, I book the chat. You show up — that’s the whole job-hunt.