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Salary: USD 250,000 - 350,000 / annual
Periodic Labs is an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs in materials, energy, and related domains. As Research Scientist, Data, you will own the evaluation and data strategy across the training stack, working at the intersection of scientific AI development and data infrastructure.
Your core responsibilities include: designing cutting-edge evaluations and benchmarks based on advanced scientific use cases; sourcing and procuring external datasets from chemistry, physics, materials science, mathematics, and laboratory instrumentation; integrating internally generated experimental data into the training pipeline; and constructing training environments for reinforcement learning. You will partner closely with computational and experimental scientists to translate complex scientific workflows into rigorous evaluations and agentic benchmarks.
You will collaborate with pretraining, midtraining, and reinforcement learning researchers to identify data requirements, then build the datasets, environments, and pipelines to deliver them. A key focus is creating a tight feedback loop between scientific use cases, model evaluation, and training data. You'll also build tooling and analysis workflows that help researchers inspect data, understand model failures, and determine which evaluations or datasets to prioritize next.
The role requires strong judgment about dataset and evaluation quality—including scientific relevance, coverage, provenance, licensing, and contamination risks. You'll need solid software and data engineering skills, including experience with large-scale data processing, dataset versioning, and lineage tracking. A research-oriented mindset is essential: you form hypotheses about data, run controlled experiments, measure model outcomes, and iterate with rigor.
Minimum qualification is a Bachelor's degree or equivalent experience. The company sponsors visas and is growing rapidly with backing from world-class investors.
