Qingpeng Z
Senior Machine Learning Engineer at ResMed
San Diego, California, United States
Summary
👤
Senior🎓
Top SchoolQingpeng Z is a Senior Machine Learning Engineer with 16 years of technical experience and over 7 years focused on building and productionizing large-scale ML systems on AWS. He designs end-to-end ML solutions and platforms, spanning MLOps, data engineering, and model deployment using tools like PyTorch, Hugging Face, Sagemaker, Airflow, and Databricks. His background combines a Ph.D. in Computer Science and genomics research at Berkeley Lab with industry roles delivering ML at ResMed, Whole Foods, MINDBODY, and Illumina, where he drove cost forecasting, promotion incrementality, and scalable inference pipelines. A pragmatic full‑stack practitioner, he contributes to bioinformatics open source (improving k-mer counting performance in the khmer repo), reflecting deep systems and algorithmic skills beyond typical ML engineering. Based in San Diego, he brings a track record of cutting cloud costs and accelerating turnaround times while building robust, observable ML platforms.
16 years of coding experience
5 years of employment as a software developer
Bachelor's degree, Physics, Bachelor's degree, Physics at Nanjing UniversityDoctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at Michigan State University
Chinese, English
Github Skills (12)
bioinformatics10
data-structures10
algorithms10
hash10
mer10
c-language10
cprogramming-language10
bloom-filter10
python10
data-structure10
k10
excel-dna6
Programming languages (7)
Github contributions (5)
dib-lab/khmer
Oct 2010 - Feb 2015
In-memory nucleotide sequence k-mer counting, filtering, graph traversal and more
Role in this project:
Back-end Developer
Back-end DeveloperContributions:54 commits, 1 PR, 6 comments in 4 years 4 months
Contributions summary:Qingpeng primarily focused on implementing and improving k-mer counting and related functionalities within the `khmer` repository, which is a bioinformatics tool. Their work involved modifying the core `hashbits.hh` and `hashbits.cc` files to implement a bloom filter, enhancing the efficiency of counting unique k-mers. The user also contributed to creating and refining Python scripts like `bloom_count.py` and `count-overlap.py`, demonstrating a focus on practical application and performance. These scripts utilize the k-mer counting libraries, enabling the analysis of DNA sequences.
memorysequencepythondnabioinformatics
qingpeng/VenmoPlus
Jun 2016 - Sep 2019
Contributions:16 commits, 68 pushes, 1 branch in 3 years 3 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.