Research, Pre-Training Data
Designs and implements methods for sourcing, curating, and analyzing large-scale pre-training datasets for AI models, blending research with production-grade data engineering. Requires Python proficiency, deep learning frameworks, and strong ML fundamentals.
About the job
What You’ll Do
- Design and implement techniques for curating, sourcing, and filtering large-scale text, code, and multimodal data.
- Develop data quality metrics and analysis to measure coverage, diversity, and representativeness across sources.
- Collaborate with research and infrastructure teams to scale data processing systems efficiently and reproducibly.
- Investigate and mitigate data risks, including privacy, safety, and licensing concerns, to ensure responsible and ethical data use.
- Continuously evaluate dataset improvements by analyzing their downstream effects on model learning and behavior.
- Publish and present research that moves the entire community forward. Share code, datasets, and insights that accelerate progress across industry and academia.
Skills and Qualifications
Minimum qualifications:
- Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX). Comfortable with debugging distributed training and writing code that scales.
- Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.
- Clarity in communication, an ability to explain complex technical concepts in writing.
Preferred qualifications:
- A strong grasp of probability, statistics, and ML fundamentals. You can look at experimental data and distinguish between real effects, noise, and bugs.
- Experience with curation, preprocessing, and analysis of large-scale text, code, or multimodal datasets.
- Prior experience in data engineering, dataset construction, or large-scale web data processing for machine learning models.
- Experience evaluating or improving training data quality and knowledge of data ethics, safety, and licensing frameworks relevant to AI dataset creation.
- Contributions to open datasets, research publications, or data tooling.
- PhD in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding; or, equivalent industry research experience.
Logistics
Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.
Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.
Skills
Python, PyTorch, TensorFlow, JAX, Machine Learning, Data Engineering, Distributed Training, Statistics, Probability, Multimodal Data
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