From bits to atoms. Backed by Accel, Lightspeed and a16z.
About the role
You will work on the most important aspect of Scientific AI creation: evaluations and data. This means constructing cutting-edge evaluations based on advanced scientific use cases, sourcing and procuring external datasets, integrating internally generated experimental data into the training stack, constructing training environments for RL. You’ll ensure that the team always has the right assets, in the right shape, to evaluate and improve AI models.
What they're looking for
- Designed evaluations, benchmarks, or RL environments for language models, agents, or scientific AI systems
- Built large-scale data pipelines for LLM pretraining, midtraining, post-training, or evaluation
- Strong judgment about dataset and evaluation quality, including scientific relevance, coverage, provenance, licensing, and contamination risks
- Strong software and data engineering skills, including familiarity with data processing at scale, dataset versioning, lineage tracking
- A research-oriented mindset: you form hypotheses about data, run controlled experiments, measure model outcomes, and iterate with rigor
- Research experience in areas such as materials science, solid state chemistry, chemistry, computational physics, semiconductors
More about this role
The most important scientific discoveries of our time won’t happen in a traditional lab. We’re an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what’s scientifically possible.
You will work on the most important aspect of Scientific AI creation: evaluations and data. This means constructing cutting-edge evaluations based on advanced scientific use cases, sourcing and procuring external datasets, integrating internally generated experimental data into the training stack, constructing training environments for RL. You’ll ensure that the team always has the right assets, in the right shape, to evaluate and improve AI models.
You will work with computational and experimental scientists to translate complex scientific workflows into rigorous evaluations and agentic benchmarks, and partner with pretraining, midtraining, and reinforcement learning researchers to identify the data models needed, then build...
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