Research Engineer – Pretraining Data

Prime Intellect

. Find, evaluate, and collect valuable training data from web, code, and technical sources .

Posted 10/16/2026full-timeRemote • California • United StatesMid-LevelSenior💰 $150,000 - $350,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates strong programming skills in Python and expertise in building reliable data-processing systems. Capable of managing large datasets and implementing effective data quality checks while collaborating with researchers on data experiments and model evaluations.

Highest-signal resume keywords

Python ProgrammingData Processing SystemsLarge Dataset ManagementData Quality DiagnosisWeb Crawling

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data ExtractionDeduplicationText ClassificationQuality ScoringSynthetic Data AnalysisData-Mixture ExperimentsCurriculum LearningDataset PreparationControlled Data ExperimentsModel Evaluation

Soft Skills

Problem OwnershipCollaboration

Tools & Technologies

SparkRayDistributed Processing Frameworks

Industry Keywords

Training Data CollectionCorpus CurationData Quality ChecksLong-Context LearningModel Training

Tech Stack

Tools & technologies

Distributed SystemsPythonRaySpark

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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