JC

Jingxiao Cai, Ph.D.

Principal Engineer, Distributed ML Runtime & Backend Execution Systems
📍 Fremont, CA (San Francisco Bay Area)
LinkedIn GitHub Google Scholar Blog Email

About

I build Dask-based distributed ML runtime systems and backend execution paths for production AutoML and GenAI workloads. At Oracle HeatWave, my work spans SQL-to-backend-runtime execution paths, secure transport, worker and process lifecycle control, abort/recovery, cluster health and scaling behavior, large-cluster reliability, and production root-cause analysis (RCA). I also contribute upstream fixes to OpenClaw, grounding my AI-agent interest in real runtime and document-extraction reliability work. Earlier research led to a VLDB AutoML publication and a granted patent on adaptive sampling for ML data reduction.

Selected Highlights

Dask-based distributed ML runtime SQL-to-backend-runtime execution paths Secure transport / mTLS / certificate hardening Worker lifecycle, abort/recovery & elasticity Large-cluster reliability & root-cause analysis OpenClaw upstream contributor VLDB AutoML publication + granted ML patent

Experience

Principal Engineer, Distributed HeatWave ML Runtime Systems
Oracle | MySQL HeatWave AutoML/GenAI
Redwood City, CA
Oct 2018 – Present
  • Built and hardened the Dask-based backend runtime behind HeatWave ML, spanning SQL-to-backend-runtime execution paths, secure transport, worker lifecycle, and service reliability as the product evolved into a production cloud service.
  • Implemented and hardened secure communication and trust paths across the ML runtime, including database-to-ML-driver transport, encrypted Dask worker communication, worker-to-worker mTLS, and certificate hardening.
  • Hardened runtime control paths for concurrency, process isolation, abort/recovery, cluster health, and elastic worker management, including readiness work at 100+ node scale.
  • Drove root-cause analysis (RCA) and reliability improvements for hard runtime failures, OOM/hybrid workload contention, and distributed execution issues across backend/runtime paths.
Data Scientist → Senior Statistical Analyst
Walmart
Bentonville, AR
Jul 2017 – Sep 2018
  • Built talent recommendation engine using Random Forest to optimize hiring for Store/Assistant Managers, reducing vacancy impact.
  • Created internal wiki on data science techniques (RegEx, imputation, Linux scripting).
Graduate Student Research Assistant
University of Oklahoma
Norman, OK
Aug 2012 – Jun 2017
  • Developed ML algorithms (ANN/Random Forest) for radar turbulence detection in collaboration with Hong Kong Observatory.
  • Optimized GPU-parallelized radar algorithms (Super-Resolution, Pulse Compression) on Nvidia Jetson.
  • Implemented target tracking for wind turbine clutters.

Open Source

Upstream Contributor
OpenClaw · AI-agent / personal-assistant platform
2026
  • Contributed 8 merged upstream PRs to OpenClaw with regression coverage, focused on AI-agent gateway/runtime reliability, tool-progress delivery, and document extraction correctness; selected PRs: #90487 (ChatGPT/Codex Responses SSE stream hardening), #92362 (single-session row metadata context), #86455 (sessions_yield abort lock release), #85652 (Gateway prompt-history stream-error filtering), #80042 (Discord verbose tool progress delivery), #51329 (Codex extraction fallback), and 2 more.
  • Listed as @anyech on the official contributors page; work includes TypeScript gateway/runtime correctness, Discord/tool-progress delivery, PDF.js/package-layout handling, and LLM-integrated document extraction.

Skills

Distributed Systems Architecture Distributed ML Runtime Systems Backend Execution Runtime Radar & Sensor Fusion C++ Python SQL Concurrency & Process Isolation Secure Protocol Design FedRAMP/FIPS-aware Secure Runtime Work Dask CUDA/RAPIDS evaluation

Education

Ph.D. in Electrical Engineering
University of Oklahoma
Dec 2017

Focus: Radar Signal Processing, GPU-accelerated signal processing

B.Eng in Electronics and Information Engineering
Sichuan University, China
2012

Publications

Oracle AutoML: A Fast and Predictive AutoML Pipeline
A Yakovlev, HF Moghadam, A Moharrer, J Cai, N Chavoshi, V Varadarajan, SR Agrawal, T Karnagel, S Jinturkar, N Agarwal
Proceedings of the VLDB Endowment, Vol 13, No 12, pp. 3166-3180, Aug 2020
Diagnosis and Classification of Typhoon-Associated Low-Altitude Turbulence Using HKO-TDWR Radar Observations and Machine Intelligence
J Cai, Y Zhang, RJ Doviak, Y Shrestha, PW Chan
IEEE Transactions on Geoscience and Remote Sensing, 2019
General Purpose Graphic Processing Unit Implementation of Adaptive Pulse Compression Algorithms
J Cai, Y Zhang
Journal of Applied Remote Sensing, Vol 11, Issue 3, 035009, 2017
Micro-Doppler Radar Signature Identification within Wind Turbine Clutter Based on Short-CPI Airborne Radar Observations
R Nepal, J Cai, Y Zhang
IET Radar, Sonar & Navigation, Vol 9, Issue 9, pp. 1268-1275, 2015
Acceleration of Advanced Radar Processing Chain and Adaptive Pulse Compression Using GPGPU
J Cai, Y Zhang, F Kong, L Li
SpringSim-HPC, 2016
Real-Time Radar Signal Processing Using GPGPU
F Kong, Y Zhang, J Cai, RD Palmer
SPIE Defense + Security, 2016
Concept Design and Feasibility Studies for a Ka-band, UAS-based Cloud Sensing Radar
J Cai, RK Mirza, Y Zhang, D Delene, JS Tilley
AMS 37th Conference on Radar Meteorology, 2015

Patents

Adaptive Sampling for Imbalance Mitigation and Dataset Size Reduction in Machine Learning
US11562178B2 (Granted Jan 24, 2023)

Contact

📧 Email
anyech@gmail.com
💼 LinkedIn
linkedin.com/in/jingxiaocai
🐙 GitHub
github.com/anyech
🎓 Google Scholar
scholar.google.com
🛂 Work Authorization
U.S. Permanent Resident