Research, Pre-Training Data
$350k - $475k • San Francisco • FullTime
Posted 2mo ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
On-site
Employment Type
FullTime
Categories
AI Research Engineer
About the job
The role
of pre-training researchers is central to our roadmap, blending research with large-scale data engineering to assemble pre-training datasets and data systems for the next generation of AI models. You will design and implement methods for sourcing, curating, and analyzing pre-training data for quality and performance, working with automated pipelines and human-in-the-loop processes. This position requires both scientific insight and production-grade code, ideal for someone who thrives at the intersection of data, machine learning, and systems, and is excited by the challenge of shaping frontier AI. The role encompasses fundamental research and practical engineering, expecting candidates to write high-performance code and read technical reports, appealing to those who enjoy deep theoretical exploration and hands-on experimentation.
Responsibilities
- 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, sharing code, datasets, and insights to accelerate progress.
Requirements
- 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, ability to explain complex technical concepts in writing.
- Strong grasp of probability, statistics, and ML fundamentals.
- 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.
Benefits
- Generous health, dental, and vision benefits
- Unlimited PTO
- Paid parental leave
- Relocation support