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Salary: EUR 100,000 - 140,000 / annual
Abundant is building infrastructure for training data at scale, positioning itself as the data equivalent to NVIDIA in the AI compute space. The founding team includes former leaders from Waymo, Google, Mercor, and AWS with deep expertise in ML, robotics, human computation, synthetic data, and reinforcement learning.
As a Member of Technical Staff in Research, you will architect the next generation of model reasoning and intelligence, operating at the frontier where research and execution collide. You will co-design strategies with top-tier researchers from leading AI labs to push state-of-the-art boundaries across diverse domains—from chemical engineering to complex legal logic—on high-stakes frontier projects.
Key responsibilities include:
- Drive foundational research and execution: architect and execute a core research agenda to discover generalizable ideas that advance model reasoning and intelligence at scale, owning the full research-to-production lifecycle and ensuring rapid deployment in live systems.
- Model alignment and data strategy: partner with world-class AI research teams to design, engineer, and iterate on high-impact datasets and large-scale benchmarking efforts that shape frontier model behavior, with focus on alignment, safety, and optimal reward signal definition.
- Autonomous problem selection: independently identify, scope, and manage long-running research projects, choosing the most impactful problems critical to scaling data for AGI/ASI.
- System infrastructure: collaborate with engineering teams on data pipelines, internal tooling, and high-performance deep learning algorithm implementations.
You will work with a majority of top AI labs, frontier startups, and F500 enterprises.
REQUIREMENTS:
- PhD in a related field (CS, ML, NLP, or equivalent) with a world-class publication record (NeurIPS, ICML, ICLR preferred).
- Proven experience shipping research directly to production and live systems, specifically in advanced post-training, distillation, and high-stakes evaluation methodologies.
- Expertise in large-scale agent systems: orchestration frameworks, tool APIs, distributed execution, observability, and logging infrastructure.
- Velocity to master complex fields and proven track record in research productization, including high-stakes evaluation and large-scale benchmarking.
- Thoughtful, strategic perspective on societal and safety impacts of deploying general-purpose AI systems.
- Experience advising on or shaping governmental policy related to AI safety and governance is preferred.
