Head of Product

Abundant•San Francisco, CA

About The Position

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.

Requirements

Experience defining product requirements and translating complex AI/ML concepts (e.g., RLHF, evaluation design) into scalable engineering and product roadmaps.
Proven track record of architecting and managing massive, high-complexity operational pipelines and vast, distributed contractor networks to drive measurable model capability improvements.
Exceptional communication skills to drive alignment between technical teams, enterprise customers, researchers, and operational experts. Must be able to distill ambiguous requirements into structured, flawless execution.
A scrappy, resourceful approach with a background in scaling complex, technical operations from the ground up, prioritizing extreme ownership and execution over corporate polish.

Responsibilities

Own end-to-end execution of Abundant’s most critical projects and owning the relationships internally/externally.
Translate ambiguous research requirements into structured workflows, manage distributed expert teams, and deliver flawless results under tight timelines.
Co-design the scope and verticals of model capabilities alongside researchers.
Deeply understand and design evaluations, RLHF (Reinforcement Learning from Human Feedback), and training data pipelines.
Act as the "voice of the user" for enterprises, distilling feedback into high-quality evaluation and training datasets.
Define the long-term vision, strategy, and roadmap for the AI model's capabilities and their delivery to enterprise customers.
Own and drive key business metrics (e.g., customer adoption, usage, ROI) that measure the impact of the model's capabilities.
Conduct ongoing market and competitive analysis to identify new product opportunities and ensure market fit.
Ownership of Identifying, recruiting, and managing domain experts to support high-fidelity data collection and insight, in partnership with TA
Driving the scaling of data set collection and manage complex operational pipelines.
Lead communication across diverse stakeholders, including enterprise customers, internal engineering teams, third-party contractors, and customer-side researchers.
Drive project success with incentives tied directly to performance and model capability milestones.