MirendilMember of Technical Staff, Pretraining Data
San Francisco · Full-time
$300K–$400K
Why join Mirendil?
In a world where this works, a drug discovery or chemistry team could run frontier-grade AI research without building a frontier lab, and Mirendil would be the reason. A $200M seed that outsizes 99% of rounds at its stage over six months came from Andreessen Horowitz, NVIDIA and Kleiner Perkins. With 11 to 50 people and 13 open roles in San Francisco, California, early hires would still shape the lab.
JustBacked's take, based on Mirendil's own site and its funding and hiring data.
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
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.