Member of Technical Staff, Research
External
Abundant · San FranciscoPrepare for this interview
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About 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.
Responsibilities
- 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.
Requirements
- 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.
Benefits
Additional Information
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.
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