5+ years of experience in data engineering or machine learning roles
Proven track record of shipping data changes in large pretraining runs
Expertise in building data pipelines at scale using Spark, Ray, or Beam
Strong proficiency in Python programming
Familiarity with PyTorch or JAX for training experiments
Hands-on experience with deduplication, quality classifiers, or data scaling laws
Responsibilities
Build data pipelines for pretraining, including repository-scale code and documentation
Enhance public data extraction, filtering, and deduplication processes
Determine data mixtures for training runs and design corresponding experiments
Direct agents to execute experiments and pipelines in parallel
Extend code-first mixtures into scientific domains to address model shortcomings
Maintain a clean and reproducible corpus as it expands
Benefits
Meaningful equity grant based on experience
Competitive benefits package
Full Job Description
The Role
We pretrain our own models and we are scaling to larger models now. Agents handle much of the engineering, but deciding what goes into training still depends on strong human judgment.
You will set the direction for what our models train on and use agents to run the ablations, pipelines, and analyses that test it. You will work closely with the researchers who own architecture and training runs.
What You'll Do
Work on building data pipelines for pretraining, including repository-scale code and technical documents.
Get more out of public data through better extraction, filtering, and deduplication of web pages, open-source code, and public datasets.
Decide the data mixture for each run, and design the ablations and data scaling experiments that justify it.
Direct agents to run those experiments and pipelines in parallel, and decide which results to trust.
Extend a code-first mixture into scientific domains, starting from where our models fall short.
Keep the corpus clean as it grows: provenance, decontamination against our evals, and reproducible versions.
About You
You have shipped a data change into a large pretraining run and can show the ablation that justified it.
You have built data pipelines at the scale of trillions of tokens with Spark, Ray, Beam, or similar, and you write strong Python.
You know PyTorch or JAX well enough to run your own training experiments.
You have hands-on experience with at least two of: deduplication, quality classifiers, mixture optimization, data scaling laws, or long-context and code data.
You can tell when an experimental result is real, and you explain your reasoning clearly in writing.
If you're excited about shaping what our models learn from, and proving it with experiments, we'd love to hear from you.
We offer a base salary of $300,000-$400,000 USD and a meaningful equity grant, depending on experience and background, along with competitive benefits.
