Research Scientist, Data

Highlights

• Own evaluation and data strategy across the scientific AI training stack.
• Translate advanced scientific workflows into rigorous evals and benchmarks.
• Build RL environments and pipelines linking experiments to model training.

Ideal candidate

Research-minded scientist-engineer who loves turning complex lab workflows into rigorous evals and datasets. You care about data quality and tight feedback loops between science and model improvement.

Requirements

• 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