Mark Ding
Founding Engineer at SourceReady
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMark Ding is a founding engineer and AI-focused builder with seven years of experience shipping full-stack and data-heavy systems across startups and scale-ups from Tesla to YC-backed ventures. He’s launched and led engineering for AI agents and product sourcing platforms, combining hands-on backend work (PySpark, Airflow, Flask) with production ML pipelines and document-vision tooling. His open-source contributions include strengthening OCR and document-extraction pipelines—adding robustness, retry logic, and HEIC/Excel support—to a vision-driven PDF-to-Markdown project, reflecting a focus on reliability at the integration layer. Comfortable with petabyte-scale ETL, time-series analytics, and embedded product work, he pairs startup speed with engineering discipline honed at Tesla and Berkeley M.Eng. A practical problem-solver, he often surfaces efficiency gains (e.g., cutting manual data-cleaning time dramatically) while iterating quickly on product-facing AI features.
7 years of coding experience
6 years of employment as a software developer
Master of Engineering - MEng EECS, Master of Engineering - MEng EECS at University of California, BerkeleyYC W23, YC W23 at Y CombinatorUniversity of California, San Diego
Chinese, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (9)
ocr10
openai10
ocra10
python10
back-end-development10
image-processing9
file-conversion9
error-handling9
performance-testing8
Programming languages (3)
Github contributions (5)
getomni-ai/zerox
Sep 2024 - May 2025
OCR & Document Extraction using vision models
Role in this project:
Back-end Developer
Back-end DeveloperContributions:13 PRs, 46 pushes, 14 branches in 8 months
Contributions summary:Mark primarily contributed to the back-end logic and structure of the project. They added parameters and statuses to the core `zerox` function, indicating modifications to the image processing pipeline. The user implemented error handling mechanisms and retry functionality, along with parameters for managing retries, indicating a focus on the robustness of the system. They also made minor adjustments to the main function, added support for Excel files and HEIC images, and included performance testing to evaluate the efficiency of the project's OCR.
ocrpdf
kailingding/AutoTSF
May 2020 - Jun 2020
Automated time-series forecasting with state-of-art machine learning and deep learning algorithms
Contributions:28 commits, 23 pushes, 1 branch in 23 days
deep-learningmachine-learningtime-series-forecasting
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