Jake Mannix
Technical Fellow, AI & Relevance at Walmart Global Tech
Seattle, Washington, United States
Summary
👤
Senior🎓
Top SchoolJake Mannix is a Technical Fellow specializing in AI, search, and recommender systems with 16 years of hands-on experience architecting distributed ML and large-scale relevance platforms across companies like LinkedIn, Salesforce, Twitter, and now Walmart Global Tech. He builds production-ready embedding and vector-similarity infrastructures, LLM-based retrieval/ranking stacks, and ML-as-a-service capabilities while leading hybrid teams of engineers and data scientists. A longtime OSS advocate and former VP of Apache Mahout, he has contributed core vector/matrix work to scalable libraries and helped productionize algorithms from Spark to HNSW-based vector search. Jake pairs deep mathematical training (MS in Mathematics, PhD-level physics background) with pragmatic distributed-systems engineering, enabling complex models to run reliably at retail and enterprise scale. He also mentors startups as an advisor and quietly runs a consulting "stealth" startup, reflecting a blend of product curiosity and entrepreneurial grit.
16 years of coding experience
21 years of employment as a software developer
PhD Physics, PhD Physics at University of WashingtonUniversity of California Santa CruzPhD Physics, PhD Physics at Stanford University
French
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (10)
algorithm10
data-structures10
javas10
algorithms10
mat10
matrix10
data-structure10
java10
linear-algebra9
unit-testing9
Programming languages (8)
Github contributions (5)
apache/mahout
Dec 2009 - Jun 2013
Mirror of Apache Mahout
Role in this project:
Back-end Developer
Back-end DeveloperContributions:67 commits in 3 years 6 months
Contributions summary:Jake's contributions primarily involved modifying and testing core Java classes within the Apache Mahout library. Commits included the addition and modification of classes related to matrix operations and sparse vector implementation, addressing inconsistencies and performance improvements. These changes involved modifications to file structures to ensure efficient use and integration of vector operations. The user also worked on unit tests, verifying correct function of the new Vector operations.
javamahoutapacheapache-mahout
industrycrashcourse/dummyjvm
Jan 2021 - Feb 2021
Placeholder JVM-based repo
Contributions:82 reviews, 17 PRs, 38 pushes in 20 days
kotlinjavajvm
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