Member of Technical Staff - Pre-Training Data

Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all.

What You’ll Do:

• Design and implement techniques for sourcing, curating, and filtering large-scale web, text, and code data, with opportunities to extend into other modalities as our models evolve.

• Develop data quality metrics and analyses that measure coverage, diversity, and representativeness across sources.

• Join the pre-training research team to design and run data experiments, working with infrastructure teams to scale the supporting data systems efficiently and reproducibly

• Investigate and mitigate data risks, including privacy, safety, and licensing concerns, to support responsible and ethical data use.

• Evaluate dataset changes through ablations and scaling experiments, tracing their downstream effects on model learning and behavior.

• Publish and present research, and share code, datasets, and insights that move the open AI community forward.

What We're Looking For:

• Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch or JAX), with experience debugging distributed workloads 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.

• Clear written communication and the ability to explain complex technical ideas.

Preferred qualifications (we encourage you to apply even if you don’t meet all of these, but at least some):

• A strong grasp of probability, statistics, and ML fundamentals, and the ability to tell 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 a related discipline; or equivalent industry research experience.