Abundant

Abundant

Member of Technical Staff, Research

San Francisco · Staff+

Sponsorship not specified$200k-$500kDetected 15 days ago
AlgorithmsMachine LearningDeep LearningData EngineeringNLPAI OrchestrationResearchLeadership

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.

Responsibilities

  • Drive Foundational Research & Execution: Architect and execute a core research agenda to discover simple, generalizable ideas that advance model reasoning and intelligence at scale.
  • 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.
  • 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.
  • Autonomously identify, scope, and manage long-running research projects, choosing the most impactful problems that are critical to scaling data for AGI/ASI.

Requirements

  • 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.
  • 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.

Skills

  • orchestration frameworks, tool APIs, distributed execution, observability, and logging infrastructure.

Compensation

  • $200,000 - $500,000++

Benefits

  • Health, dental, vision + flexible PTO
  • System Infrastructure: Collaborate closely with engineering teams on data pipelines, internal tooling, and high-performance deep learning algorithm implementations.
  • 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).
  • Sizable performance bonus tied to project and company milestones

Company info

  • 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.
  • 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.
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