Research Scientist, Data

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

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

Minimum education: Bachelor’s degree or similar experience

Location: Menlo Park, CA or Montreal, Canada. (Soon: San Francisco, too)

Compensation: $250,000-350,000 + equity

Visa sponsorship: Yes, we sponsor visas.