Xujing “James” Mao — engineer

I make systems answer in milliseconds
and AI answer correctly.

I’m a CS undergrad at Georgia Tech, moving from Sydney. I most recently built real-time voice AI at Voqo, and I’m drawn to problems where latency, correctness and scale all have to be true at once — and I like proving it with numbers.

Open to internshipsSydney → AtlantaIntelligence & Systems Architecture
Real-time signalLIVE
render 16.7 ms60 fpsmove across ↑
50
B2B users on live voice AI
500+
live call touchpoints handled
2M+
rows queried at sub-200 ms
63%
faster hyperparameter tuning
IEEE
published co-author, 2024

01 — About

Chinese-Australian, competitive-programming raised, obsessed with the fast path.

I grew up in Sydney and cut my teeth on olympiad problems — the kind where a clever bound or the right data structure is the difference between an answer and a timeout. That instinct never left. Today it shows up as retrieval that stays off the latency path, schemas that skip migrations, and tuning loops that finish in a third of the time.

At Voqo I worked across the stack of a live voice agent: the retrieval that grounds it, the evals that keep it honest, and the call orchestration that hands a caller to a human without dropping the line. I care about the boring guarantees — idempotency, precision, reconnection — because that’s what “real-time” actually costs.

B.S. CS · Georgia TechExpected May 2029ValedictorianUSACO Silver
identityavailable
Xujing “James” Mao
Low-latency systems & AI agents
nowGeorgia Tech · CS
basedSydney → Atlanta
langsPy · C++ · TS

02 — Selected work

Four systems, one throughline: make it fast and make it right.

Filter work by category
01Voqo AI · Sydney
May – Jul 2026

A voice agent that answers in real time — and answers correctly.

Document-grounded retrieval, automated evaluation, and event-driven call orchestration for a production voice agent used by B2B real-estate teams.

retrieval path · hybrid fusion
50
B2B users, live
500+
live call touchpoints
18
appraisals booked from calls
stack
PythonFastAPIMongoDB AtlasRedisTwilioWebSocketdeepeval
02Personal build
Feb – May 2026

An Airtable-class data grid that stays sub-200 ms at 2M+ rows.

A collaborative database UI engineered for scale — read virtualization, a hybrid relational/JSONB schema, and a typed view-query compiler.

2M+
rows · sub-200 ms queries
100K
row inserts in ~15 s
0
migrations for new columns
stack
TypeScriptPostgreSQLJSONBTanStack VirtualLexoRank
03Nanjing Univ. of Science & Tech · Remote
Apr – Sep 2024

CDC-YOLO — making gear-defect detection fit on an edge NPU.

A faster, lighter YOLOv8 variant for gear-defect detection on the Rockchip RK3588 edge NPU. Published at IEEE ICSMD 2024.

IEEE
ICSMD 2024, co-author
RK3588
edge NPU target
YOLOv8
lighter & faster
stack
PythonYOLOv8Computer VisionRK3588 NPU
04Pioneer Research Program · Northwestern
Mar – Aug 2024

Hyperparameter tuning as a search problem — 63% less wall-clock.

A binary-search-inspired heuristic for CNN hyperparameters that lets you choose your own accuracy–time operating point.

63%
faster than grid search
VGG16
on CIFAR-100
1
research paper authored
stack
PythonTensorFlow / KerasVGG16CIFAR-100

03 — Stack

The tools I reach for, grouped by how I think.

Languages

PythonC++TypeScript / JSSQL

AI & ML

RAG (dense + lexical)Reciprocal Rank FusionEmbeddings & vector searchLLM-as-judge (deepeval)Prompt engineeringTensorFlow / KerasComputer vision (YOLO)

Systems & Data

PostgreSQL (JSONB)MongoDB Atlas Vector SearchRedisFastAPIWebSocket / SSEEvent-driven pipelinesGCP Cloud TasksTwilioReact / React NativeGit

Recognition

Valedictorian — The King’s SchoolUSACO SilverAustralian Informatics Olympiad — SilverIEEE ICSMD 2024 — publishedPioneer Research — Northwestern

04 — Contact

Let’s build something that has to be fast.

I’m looking for internships where correctness and latency both matter. If that’s you, the quickest path is a 15-minute call — or just email me.

xjmao2008@gmail.comBook a 15-min call ↗