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Reflection AI is a research lab building open foundational models to make intelligence accessible to everyone. As a Member of Technical Staff on the Pre-Training Data team, you will work on the critical infrastructure and research that powers large-scale AI model development.
You will design and implement techniques for sourcing, curating, and filtering large-scale web, text, and code data, with opportunities to extend into other modalities as models evolve. You'll develop data quality metrics and analyses that measure coverage, diversity, and representativeness across sources. Working closely with the pre-training research team, you'll design and run data experiments while collaborating with infrastructure teams to scale supporting data systems efficiently and reproducibly.
A key part of the role involves investigating and mitigating data risks, including privacy, safety, and licensing concerns, to support responsible and ethical data use. You'll evaluate dataset changes through ablations and scaling experiments, tracing their downstream effects on model learning and behavior. You're also expected to publish and present research, and share code, datasets, and insights that move the open AI community forward.
This is a high-impact role on a talent-dense team where you'll help define the future of open foundational models and the company itself.
REQUIREMENTS:
- Proficiency in Python and familiarity with at least one deep learning framework (PyTorch or JAX)
- Experience debugging distributed workloads and writing code that scales
- Bachelor's degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or related discipline with strong theoretical and empirical grounding
- Clear written communication and ability to explain complex technical ideas
PREFERRED QUALIFICATIONS:
- Strong grasp of probability, statistics, and ML fundamentals; ability to distinguish real effects from noise and bugs in experimental data
- Experience with curation, preprocessing, and analysis of large-scale text or code datasets; exposure to multimodal data is a plus
- Background in data engineering, dataset construction, or large-scale web data processing for machine learning
- Experience evaluating or improving training data quality, and knowledge of data ethics, safety, and licensing frameworks relevant to AI datasets
- Contributions to open datasets, research publications, or data tooling
- PhD in Computer Science, Machine Learning, Physics, Mathematics, or related discipline; or equivalent industry research experience
