PERIODIC LABS
Tech / AI / Software
ResearchEngineer-Data
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Research Engineer - Data at PERIODIC LABS. Skills: Data strategy, Data pipelines, Data quality, Distributed data processing, Dataset versioning, Python engineering. Build and drive the data foundation for research efforts. Own data strategy end-to-end”
Industry & Context.
Translate data needs into pipeline requirements
Visa sponsorship
What They're Looking For.
Must Have
Experience building large-scale data pipelines for LLM pretraining or midtraining, including web-scale or scientific corpora, Expertise in data quality techniques such as exact and fuzzy deduplication (MinHash, SimHash), perplexity filtering, classifier-based quality scoring, and PII scrubbing, Experience working with diverse scientific data formats — papers, patents, structured databases, simulation outputs, lab instrument exports — and normalizing them for model consumption, Experience with distributed data processing frameworks such as Apache Spark, Ray, or Dask at multi-terabyte to petabyte scale, Familiarity with dataset versioning, lineage tracking, and reproducibility tooling such as DVC, Delta Lake, or custom solutions, Experience sourcing and evaluating third-party datasets, including licensing considerations and quality assessment, Python engineering skills and comfort building production-quality tooling in a research environment, Experience collaborating directly with ML researchers to translate data needs into pipeline requirements and back again, A research-oriented mindset — you run experiments on data, measure outcomes, and iterate with rigor
Nice to Have
Experience curating scientific datasets specifically for domain-adaptive continued pretraining or instruction tuning, Familiarity with synthetic data generation methods, including model-generated data pipelines and quality verification, A background in a physical science or engineering discipline that informs how you think about scientific data quality and structure, Experience with multimodal data — integrating text, structured numerical data, molecular representations, or spectral data into unified training pipelines
What You'll Do.
Build and drive the data foundation for research efforts
Own data strategy end-to-end
Source and procure external datasets
Integrate internally generated experimental data into the training stack
Ensure the team always has the right data — in the right shape — to train and improve frontier models
Collect and organize diverse data sources
Improve data quality through deduplication and preprocessing
Ensure new experimental results are incorporated in a structured
Own data strategy across the training stack — identifying gaps
evaluating new sources
and shaping the overall data roadmap
and procure external datasets across scientific domains
Build and maintain robust pipelines for ingesting
and versioning large-scale datasets from heterogeneous sources
Design and implement data quality systems
Integrate internally generated experimental data — from lab instrumentation
and model outputs — into the training stack
Build tooling that makes it easy for researchers to inspect
and understand the data
Instrument data pipelines with metadata
Collaborate with pretraining and midtraining engineers on token budget management
and curriculum design
Stay current with research on data-efficient training
synthetic data generation
and data selection methods — and bring relevant ideas into production
How You'll Work.
Team & Collaboration
Collaborate with pretraining and midtraining engineers on token budget management, data mixing ratios, and curriculum design; Collaborate directly with ML researchers to translate data needs into pipeline requirements and back again
Full Job Description
ABOUT PERIODIC LABS 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. ABOUT THE ROLE You will build and drive the data foundation for our research efforts. This means owning data strategy end-to-end: sourcing and procuring external datasets, integrating internally generated experimental data into the training stack, and ensuring the team always has the right data — in the right shape — to train and improve frontier models. This role sits at the intersection of data engineering, research infrastructure, and strategy. You will work closely with pretraining, midtraining, and RL researchers to understand what data the models need, then build the pipelines and systems to get it there. The work spans collecting and organizing diverse data sources, improving data quality through deduplication and preprocessing, and ensuring that new experimental results are incorporated in a structured, repeatable way that makes them useful for model development. WHAT YOU’LL DO - Own data strategy across the training stack — identifying gaps, evaluating new sources, and shaping the overall data roadmap in collaboration with research leads - Source, evaluate, and procure external datasets across scientific domains including chemistry, physics, materials science, mathematics, and lab instrumentation - Build and maintain robust pipelines for ingesting, processing, and versioning large-scale datasets from heterogeneous sources - Design and implement data quality systems including deduplication, domain classification, quality filtering, and format normalization at scale - Integrate internal
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