Eric Lundquist
Machine Learning Engineer at Hightouch
Oakland, California, United States
Summary
👤
Senior🎓
Top SchoolEric Lundquist is a Machine Learning Engineer with 11 years of experience building production-grade AI systems, currently focusing on AI decisioning and LLM agents. He has a proven track record designing and deploying multi-modal document processing, RAG pipelines, fine-tuned models, and multi-agent workflows that turn complex business problems into reliable software products. At BCG X he led teams that slashed contract review times from hours to minutes with LLM-powered applications and built cross-cloud MLOps and deployment tooling for enterprise scale. His background spans data engineering, distributed systems, and backend services—having implemented bandit-based personalization, large-scale Spark pipelines, and inference endpoints across AWS, GCP, and Azure. A Northwestern and Haverford alum based in Oakland, he combines rigorous experimental design from his policy research roots with hands-on MLOps craftsmanship and a penchant for practical, measurable impact.
10 years of coding experience
14 years of employment as a software developer
Bachelor's degree Mathematical Economics, Bachelor's degree Mathematical Economics at Haverford CollegeMaster’s Degree Data Science, Master’s Degree Data Science at Northwestern University
English, Spanish
Github Skills (28)
implicit10
collaborative-filtering10
recommender-system9
personalization9
factorization9
application9
helper-methods8
machine-learning8
fine-tuning7
ranking7
data-structures7
twitter-sentiment-analysis6
pyspark6
vector-database6
llm6
Programming languages (1)
Github contributions (5)
etlundquist/rankfm
May 2020 - Jul 2020
Factorization Machines for Recommendation and Ranking Problems with Implicit Feedback Data
Contributions:8 releases, 33 commits, 48 PRs in 2 months
implicitimplicit-feedbackfactorizationmachinesproblems
etlundquist/SchneiderExperience
Jun 2016 - Dec 2016
Contributions:3 commits, 2 pushes, 1 branch in 6 months
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