Research Engineer – Pretraining Data

Job not on LinkedIn

🔥 10 minutes ago

🏄 California – Remote

🏄 California – Remote

💵 $150k - $350k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

📚 Research Engineer

👻 Ghost score 0%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Prime Intellect

WebsiteLinkedInAll Job Openings

1 - 10 employees

🤖 Artificial Intelligence

☁️ SaaS

Artificial Intelligence • SaaS • Cloud Computing

Prime Intellect is a company focused on democratizing AI development by providing scalable and decentralized computing resources for training models. Their platform allows users to find and share global compute resources, enabling the training of state-of-the-art models through distributed clusters. They promote the collective ownership of AI innovations, including language and scientific models. Prime Intellect also offers a range of GPU options to facilitate affordable and efficient model training. They aim to advance decentralized training research and open-source AI development on a global scale.

📋 Description

• Find, evaluate, and collect valuable training data from web, code, and technical sources • Build scalable pipelines that turn raw data into training-ready corpora through extraction, cleaning, deduplication, and filtering • Develop checks for data quality, coverage, and contamination, and use them to improve collection and processing • Work with researchers on data mixtures and sampling strategies for pretraining and midtraining • Prepare and evaluate data for long-context learning, reasoning, and coding, testing synthetic data where useful • Run controlled data experiments and use model evaluations to decide which sources, mixtures, and processing choices are worth scaling • Make large-scale data processing efficient, reproducible, and recoverable, with clear source provenance and dataset versioning

🎯 Requirements

• Strong programming skills in Python and a record of building reliable data-processing or distributed systems • Experience working with large datasets and managing the practical tradeoffs of storage, compute, throughput, and reproducibility • Ability to diagnose data-quality issues and translate research goals into measurable checks and experiments • Comfort owning an evolving technical problem end to end and working closely with researchers to test assumptions and improve the system • Experience with web crawling, document extraction, code-data collection, or large-scale corpus curation • Experience with deduplication, text classification, quality scoring, synthetic data, or training-data contamination analysis • Experience with data-mixture experiments, curriculum learning, or measuring how dataset changes affect model performance • Experience preparing datasets for language-model pretraining, continued pretraining, or midtraining is nice to have • Work with distributed processing frameworks such as Spark, Ray, or comparable systems is nice to have • Experience with multilingual, mathematical, scientific, or repository-level code data is nice to have • Familiarity with tokenization, training data loaders, and the needs of large model-training runs is nice to have

🏖️ Benefits

• Equity incentives • Flexible work arrangements, with the option to work remotely or in-person at our offices in San Francisco • Visa sponsorship • Relocation assistance for international candidates • Quarterly team off-sites • Hackathons • Conferences and learning opportunities