Jim Kleckner
Sr. Distinguished Technologist at Hewlett Packard Enterprise
Palo Alto, California, United States
Summary
👤
Senior🎓
Top SchoolJim Kleckner is a seasoned technology leader and Sr. Distinguished Technologist based in Palo Alto with over a decade of recent hands-on experience and a career spanning foundational roles in high-performance trading systems, analytics, and startup cofounding. He has led engineering, product, and operations teams to build low-latency, auditable transaction platforms and scalable analytics at companies like Currenex, CloudPhysics, and Hewlett Packard Enterprise. An organizer and motivator of high-powered teams, he combines executive board experience with deep technical roots—holding a PhD in EECS from UC Berkeley and early experience founding Cadence’s simulation efforts. Jim contributes to open-source machine learning tooling (notably maintenance and quality work on the pomegranate probabilistic modeling library), showing attention to robustness, serialization, and memory safety. He’s equally comfortable setting architectural strategy, recruiting top talent, and rolling up his sleeves to fix code and tests. Colleagues can expect a leader who pairs rigorous academic training with pragmatic product delivery and operational discipline.
11 years of coding experience
26 years of employment as a software developer
PhD, EECS, PhD, EECS at University of California, BerkeleyCalifornia Institute of Technology
Stackoverflow
1,108reputation
115kreached
9answers
10questions
anaconda
top-5%
Github Skills (17)
python10
machine-learning10
cython10
testing9
serialization9
data-serialization9
anaconda9
yaml8
json8
code-generation6
type-inference6
conda6
makefile6
scala6
django6
Programming languages (17)
Github contributions (5)
jmschrei/pomegranate
Nov 2017 - Feb 2019
Fast, flexible and easy to use probabilistic modelling in Python.
Role in this project:
ML Engineer
ML EngineerContributions:2 reviews, 17 commits, 24 PRs in 1 year 2 months
Contributions summary:Jim primarily contributed to the maintenance and improvement of the `pomegranate` library, which focuses on probabilistic modeling. The contributions involved fixing typos, updating documentation, and adding code to support distributions, and implementing methods to handle JSON and YAML serialization. The user also added unit tests and addressed memory leaks, demonstrating their focus on code quality and stability within the machine learning library.
pythonparallelmachine-learningprobabilistic-programmingprobability
jkleckner/spark
Feb 2015 - Nov 2020
Mirror of Apache Spark
Contributions:23 pushes, 17 branches, 4 tags in 5 years 9 months
spark-mlapachebig-datasparkscala
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