Member of Technical Staff, Pretraining Data
United States, CAFull-time$300–400K/yrPosted 1 day agoStill listed 1 day ago
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Job overview
Mirendil is building frontier AI models and is scaling its pretraining efforts. The Member of Technical Staff, Pretraining Data will shape training data, use agents to run experiments and pipelines, and work with researchers responsible for model architecture and training runs. The role focuses on data mixtures, public-data processing, scientific domains, and maintaining clean, reproducible corpora.
Skills & qualifications
Skills
Qualifications
Full job description
Mirendil Mirendil is a tech-first company focused on solving core bottlenecks that unlock step-change acceleration across science and technology. Our first goal is to democratize frontier AI R&D across scientific disciplines. We are building a frontier AI research company and training our own models end-to-end.
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
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Work on building data pipelines for pretraining, including repository-scale code and technical documents.
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Get more out of public data through better extraction, filtering, and deduplication of web pages, open-source code, and public datasets.
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Decide the data mixture for each run, and design the ablations and data scaling experiments that justify it.
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Direct agents to run those experiments and pipelines in parallel, and decide which results to trust.
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Extend a code-first mixture into scientific domains, starting from where our models fall short.
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Keep the corpus clean as it grows: provenance, decontamination against our evals, and reproducible versions.
About You
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You have shipped a data change into a large pretraining run and can show the ablation that justified it.
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You have built data pipelines at the scale of trillions of tokens with Spark, Ray, Beam, or similar, and you write strong Python.
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You know PyTorch or JAX well enough to run your own training experiments.
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You have hands-on experience with at least two of: deduplication, quality classifiers, mixture optimization, data scaling laws, or long-context and code data.
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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.
How this job compares
Set against the 1,957 open Machine Learning jobs on Olive Jobs, counted November 10, 2026.
- Pay
- This posting$300–400K/yr
- California median$193–286K/yr343 salary ranges
- US median$155–250K/yr1,478 salary ranges
This range's midpoint, $350K, is 44% above the California median midpoint ($243K) — in the top quarter of Machine Learning salary ranges in California.
- Openings
- Olive Jobs lists 497 open Machine Learning jobs in California and 1,957 across the US. 8% of them nationwide are listed as remote or flexible.
- Experience
- Machine Learning postings that state a minimum ask for 5 years of experience at the median; the middle half ask for 3 to 8 years (1,421 postings).
- Education
- 718 of the 1,957 open Machine Learning jobs (37%) ask for a bachelor's degree or higher.
Counted across the open, public US postings on Olive Jobs on November 10, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.
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