Tim Hopper
Machine Learning Infrastructure Engineer at Spotify
Raleigh, North Carolina, United States
Summary
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Rockstar🎓
Top SchoolTim Hopper is a Machine Learning Infrastructure Engineer with 14 years of experience building production-grade ML systems and platforms, currently supporting audio intelligence at Spotify. He has led platform and feature store development for fraud, risk, and lending use cases at Varo and architected serverless feature lakes and pipelines for malware and streaming analytics at BlackBerry and Distil. Tim blends deep applied math and operations research training with hands-on software engineering—vectorizing LDA implementations and contributing notable improvements to streamparse for Apache Storm. He specializes in democratizing feature engineering, productionizing models at scale, and mentoring teams on software best practices and reproducible tooling. Based in Raleigh, NC, he pairs academic rigor with practical trade-offs, from fast MCMC for HDP-LDA to operational monitoring and log-tail optimizations. Colleagues rely on him to translate complex probabilistic models into maintainable, observable infrastructure.
13 years of coding experience
12 years of employment as a software developer
Bachelor of Science Mathematics, Bachelor of Science Mathematics at Grove City CollegeMaster's degree Operations Research, Master's degree Operations Research at North Carolina State UniversityMathematics, Mathematics at University of Virginia
Stackoverflow
945reputation
65kreached
5answers
9questions
Github Skills (27)
algorithm10
storm10
code-optimization10
algorithms10
python10
apidoc10
numpy10
api10
optimisation10
numerical-optimization10
rest-api10
optimization10
machine-learning9
http-request9
refactoring8
Programming languages (17)
Github contributions (5)
pystorm/streamparse
Aug 2014 - Jul 2016
Run Python in Apache Storm topologies. Pythonic API, CLI tooling, and a topology DSL.
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:30 commits, 3 PRs, 2 pushes in 2 years
Contributions summary:Tim significantly contributed to enhancing the `streamparse` project, primarily focused on the `stats` functionality for the Apache Storm topologies. They implemented features to display topology, cluster, and component-level statistics, leveraging the Storm UI API. Moreover, the user refactored the stats code for better organization and efficiency. They also streamlined API requests and improved the log tailing process. The user demonstrated skills in Python, integrating with external libraries like `requests` and `prettytable`, and demonstrated expertise in understanding and working with Apache Storm APIs.
apipythonsingerdslapache
lda-project/lda
Aug 2015 - Aug 2015
Topic modeling with latent Dirichlet allocation using Gibbs sampling
Role in this project:Data Scientist
Data ScientistContributions:13 commits, 3 PRs, 4 comments in 5 days
Contributions summary:Tim focused on optimizing the `LDA` (Latent Dirichlet Allocation) model implementation. They refactored the code for improved clarity and efficiency, including breaking down methods, renaming variables, and vectorizing loops. The user made several commits to improve performance by reducing unnecessary computations and computation steps. They also addressed documentation issues.
gibbsallocationlatent-dirichlet-allocationgibbs-samplingtopic-modeling
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