Member of Technical Staff, Pretraining Data

Digitailor; United States; Presencial; USD 300.000 - 400.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Equity grant

Competitive benefits

Descripción de la vacante

Mirendil Mirendil is seeking a data-driven engineer to define training data inputs and manage large-scale pretraining experiments. You will orchestrate data pipelines, ablations, and parallelized runs, collaborating with researchers on model architecture and training runs.

You will justify data choices with rigorous experiments, ensuring data provenance and reproducibility as the corpus scales. A competitive equity grant accompanies a base salary in a high-performance, research-focused

Formación

• Shipped a data change into a large pretraining run.
• Built data pipelines at the scale of trillions of tokens with Spark, Ray, Beam.
• Strong Python; experience with PyTorch or JAX for training experiments.
• Able to justify ablations and experimental results with writing.

Responsabilidades

• Set the direction for what data the models train on.
• Design ablations and data scaling experiments and justify choices.
• Direct agents to run experiments and pipelines in parallel.
• Maintain provenance and reproducible data versions for training runs.

Conocimientos

Python

PyTorch

JAX

Spark

Data pipelines

Experiment design

Data deduplication

Data scaling

Herramientas

Spark

Ray

Beam

Descripción del empleo

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.