Research Engineer - Pretraining Data

Prime Intellect • San Francisco, CA

2d • $150,000 - $350,000

About The Position

Prime Intellect is building an open superintelligence stack: infrastructure that gives AI teams access to compute, environments, evaluations, secure sandboxes, high-performance training, and deployment. Its Lab platform supports post-training at frontier scale, including supervised fine-tuning, reinforcement learning, tool use, and agent workflows. The company is also developing open frontier models and the full-stack platform used by its research team.

The Research Engineer - Pretraining Data will build the data behind Prime Intellect’s next generation of open models. The role involves finding valuable data sources, converting them into training-ready corpora, and collaborating with researchers to test which data improves model performance. The work will shape broad pretraining as well as data for reasoning, coding, and long-context capabilities.

Requirements

• Strong programming skills in Python and a record of building reliable data-processing or distributed systems.
• Experience working with large datasets and managing practical tradeoffs involving 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.
• Relevant experience may include web crawling, document extraction, code-data collection, or large-scale corpus curation.
• Relevant experience may include deduplication, text classification, quality scoring, synthetic data, or training-data contamination analysis.
• Relevant experience may include data-mixture experiments, curriculum learning, or measuring how dataset changes affect model performance.

Nice To Haves

• Experience preparing datasets for language-model pretraining, continued pretraining, or midtraining.
• Work with distributed processing frameworks such as Spark, Ray, or comparable systems.
• Experience with multilingual, mathematical, scientific, or repository-level code data.
• Familiarity with tokenization, training data loaders, and the needs of large model-training runs.

Responsibilities

• Find, evaluate, and collect valuable training data from web, code, and technical sources.
• Build scalable pipelines to convert 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 determine 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.

Benefits

• Cash compensation range of $150,000–$350,000, including equity incentives.
• Flexible work arrangements, with the option to work remotely or in person at the San Francisco offices.
• Visa sponsorship and relocation assistance for international candidates.
• Quarterly team off-sites, hackathons, conferences, and learning opportunities.
• Opportunity to work with a talented, hard-working, mission-driven team focused on leveraging technology to accelerate science and AI.

What This Job Offers

• Job Type: Full-time
• Education Level: No Education Listed