Daniel Galbraith is an AI/ML product leader and former computational linguist with a decade of experience building high-impact ML systems at Google, where he currently rotates as a Product Manager in Ad Traffic Quality. He has driven multi-org migrations, launched a user-harm metric pipeline that sized ~10B high-risk impressions, and delivered >$119M ARR by implementing LLM and human-in-the-loop data pipelines that measurably improved ad classification precision. Trained as a Stanford PhD linguist and seasoned NLP researcher (Amazon Lab126, Mosaix.ai), he blends deep language-science expertise with program-level execution to solve data, labeling, and model-quality bottlenecks. He鈥檚 known for turning research prototypes into production datasets (>800K examples) and for negotiating cross-org policy and transparency improvements that impact advertiser-facing surfaces. Based in Menlo Park, he combines academic rigor in linguistics with product instincts to reduce harm and operational costs across large-scale ad systems.
10 years of coding experience
7 years of employment as a software developer
Visiting researcher (2 months), Visiting researcher (2 months) at The University of the Faroe Islands
Erasmus exchange, Erasmus exchange at Universit茅 Paris-Sorbonne
High School GCE A-levels in English French Spanish and Latin, High School GCE A-levels in English French Spanish and Latin at Royal Belfast Academical Institution
Master's Degree MPhil in Theoretical and Applied Linguistics, Master's Degree MPhil in Theoretical and Applied Linguistics at University of Cambridge
Doctor of Philosophy (Ph.D.) Linguistics, Doctor of Philosophy (Ph.D.) Linguistics at Stanford University
Python for generating random words based on a predefined phonology
Contributions:3 releases, 41 PRs, 76 pushes in 6 years 9 months
pythonrandom-generationrandom-wordsphonologywords
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