Research Scientist, Data

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 the datasets, environments, and pipelines to deliver it. Your goal will be to create a tight feedback loop between scientific use cases, model evaluation, and training data.

What You’ll Do

• Own the evaluation and data strategy across the training stack, identifying capability gaps and shaping the roadmap with leads of physical science and AI research
• Work with domain experts to translate advanced scientific workflows into rigorous evals, benchmarks, and RL environments
• Source, evaluate, and procure external datasets across chemistry, physics, materials science, mathematics, simulations, and laboratory instrumentation
• Build robust pipelines to ingest, clean, and transform for training large-scale datasets from heterogeneous sources
• Build tooling and analysis workflows that help researchers inspect data, understand model failures, and determine which evaluations or datasets to develop next

You Will Thrive in This Role If You Have

• 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

Mechanics

Minimum education: Bachelor’s degree or similar experience