Checked against the employer's source on October 15, 2026. Details come from the employer's posting; confirm them on the application page.
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Job Description
ABOUT ABUNDANT
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.
THE ROLE
As a Research Intern 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 researchers who are already investigating the frontier and are looking to apply their learnings alongside world-class peers.
WHAT YOU’LL DO
Drive Foundational Research & Execution: Architect and execute a core research agenda to discover simple, generalizable ideas that advance model reasoning and intelligence at scale. This includes owning the full research-to-production lifecycle and ensuring rapid deployment of your work in live systems.
Model Alignment & Data Strategy: Partner with the world's most advanced AI research teams to design, engineer, and iterate on high-impact datasets and large-scale benchmarking efforts that shape how frontier models behave, specifically focusing on critical alignment, safety, and defining optimal reward signals.
Autonomous Problem Selection: Autonomously identify, scope, and manage long-running research projects, choosing the most impactful problems that are critical to scaling data for AGI/ASI.
System Infrastructure: Collaborate closely with engineering teams on data pipelines, internal tooling, and high-performance deep learning algorithm implementations.
WHO YOU ARE
Exceptional Research Depth & Strategic Vision: Research expertise in a related field (e.g., CS, ML, NLP) ideally with that work published at a major conference (NeurIPS, ICML, ICLR). You must possess the velocity to master complex fields and a proven track record in the productization of research, encompassing high-stakes evaluation, large-scale benchmarking, and product feature development.
Proven experience shipping research directly to production and live systems, specifically focusing on advanced post-training, distillation, and high-stakes evaluation methodologies.
Strategic & Ethical Leadership: You possess a thoughtful, strategic perspective on the societal and safety impacts of deploying general-purpose AI systems. Experience advising on or shaping governmental policy related to AI safety and governance (e.g., House of Lords or Online Safety Bill initiatives) is required.
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