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Member of Technical Staff, Research

Compensation
$135,000–$350,000/year

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 the Member of the Technical Staff, you are the "PM of the model," architecting 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 technical visionary with a "founder mentality" and a bias for extreme ownership, infiltrating unknown domains—from chemical engineering to complex legal logic—to extract insights for high-stakes frontier projects that others find impossible.


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 : A PhD in a related field (e.g., CS, ML, NLP) with a world-class publication record (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.

  • 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.

COMPENSATION

Base Salary

$200,000 - $500,000++

Cash Bonus

Sizable performance bonus tied to project and company milestones

Equity

Meaningful early-stage grant

Benefits

Health, dental, vision + flexible PTO

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Job Details

Category
Research
Employment Type
Full Time
Location
San Francisco, CA
Posted
Compensation
$135,000 - $350,000 per year

About Abundant

Hello! 👋 We are a team of former ML engineers, founders, roboticists and ops leads who obsess about data and its impact on safe, reliable AI. We specialize in creating environments and datasets for RL by leveraging our experience in simulation and model training. By the numbers: • Powering 3 of the top 6 global AI labs and multiple Fortune 500 enterprises • Billions of training tokens generated each month, 2x month over month • Exclusive, global network of over 500 domain experts We believe humans are inherently creative, and thrive by pushing the frontier. We are working towards an abundant future--one where everyone has access to infinite intelligence, services and goods. Based in San Francisco, CA. We enjoy good food and good company. -- more info below -- Abundant is building the NVIDIA of training data. AI models rely on two fundamental ingredients: compute and 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 second-time founders, ML engineers and data leads from Waymo, Google, Meta and AWS. Our team has previously collaborated with DeepMind to classify hate speech in YouTube videos, trained SOTA models for self-driving, and scaled data pipelines with thousands of human annotators. Our pioneering work in human computation, synthetic data, imitation learning and RL give us a solid 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 new, diverse, and high-quality datasets, making training data more important and scarce than ever before. What happens if we succeed? Abundant will be the core enabler for AGI and beyond. Most of the challenges in model training are already solved. What’s missing is the data necessary to move from general knowledge to domain expertise; from chatbots to agents; and from digital intelligence to physical AI. Ask any AI researcher or roboticist: the core bottleneck to progress is the availability of data, i.e. “abundant data”. Abundant works with the most advanced AI labs and startups, as well as F500 enterprises.

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