Ming Chong Lim.

Quantitative Developer · DRW

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About

I'm a Quantitative Developer at DRW in Singapore. I graduated from Carnegie Mellon University with a Master's in Machine Learning.

While ML is my main focus, I have a broad range of interests spanning across multiple computing disciplines, and in general, I enjoy solving challenging problems and devising elegant solutions to them.

I'm passionate about both learning and teaching. I served as a teaching assistant for various modules over 6 semesters at both NUS and CMU.

Experience

Oct 2025 – Present

Quantitative Developer

DRW

Quantitative development spanning compute-graph frameworks, trading systems, and internal tooling.

Extended a Python compute-graph framework with C++ bindings (nanobind), adding cycle detection, benchmarking, GDB debugging, and improved Python typing; achieved 2–20x speedup on graph operations
Built a fault-tolerant intraday fill ingestion pipeline over TCP websocket with SOD-replay-based restart recovery, ensuring consistent live position state across disconnects
Extended a real-time pricing and execution-parameter engine to support China markets, integrating market data, trader signals, position/locate feeds, and static reference data; also supported feature requests from crypto and long-short rebalancing desks
Built a Hyperliquid liquidation monitor (Dash + Redis) tracking ~20k wallets with distance-to-liquidation metrics + liquidation heatmaps
Built an internal Slack support bot using the Claude agent SDK with semantic search over 8 codebases, auto-generated Bloomberg field knowledge, and MCP-gated access to live logs and data streams
Reduced CI build times by ~20% via build-environment caching and compression tuning

Full-time

Aug 2024 – Sep 2025

Cyber Security Engineer

DSO National Laboratories

Low-level systems work on QEMU/KVM and reverse engineering tooling.

Low-level modifications of QEMU/KVM source code to support Intel Processor Trace and other functionalities
Developed a Ghidra plugin to perform Struct and Enum recovery from disassembled code

Full-time

May 2024 – Aug 2024

Quantitative Developer Intern

DRW

Parallelized Python codebases and built regression testing frameworks for quant research.

Parallelized and refactored several parts of a Python code base used to support Quantitative Researchers, increasing scalability and reducing latency by 30%
Found and fixed a high-latency-impact, low-reproducibility bug that occurred in production
Developed a framework for regression testing and reproducing production bugs in a semi-realistic setting
Supported other team's C++ operations, and wrote small scripts to automate processes

Internship

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Education

2023 – 2024

Carnegie Mellon University

Master's in Machine Learning

School of Computer Science, Pittsburgh, PA

GPA: 4.07 / 4.00

Research: VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks
15-642 Machine Learning Systems
10-617 Intermediate Deep Learning
10-715 Advanced Introduction to Machine Learning
10-718 Machine Learning in Practice
16-726 Learning-Based Image Synthesis
16-720 Computer Vision
36-705 Intermediate Statistics
10-725 Convex Optimization
10-708 Probabilistic Graphical Models

2019 – 2023

National University of Singapore

BComp in Computer Science with Honours

AI Focus Area (Distinction)

GPA: 4.88 / 5.00 (3.92 / 4.00)

Dean's List: AY2021/2022 Sem 1, AY2020/2021 Sem 1
Top student: CS4243 Computer Vision (156 students), CS2106 Operating Systems (403 students), CS2103T Software Engineering (338 students)
CS4243 Computer Vision and Pattern Recognition (A+)
CS3241 Computer Graphics (A)
CS4248 Natural Language Processing (A-)
CS3243 Introduction to Artificial Intelligence (A-)
CS2106 Introduction to Operating Systems (A+)
CS3230 Design and Analysis of Algorithms (A)
CS2040S Data Structures and Algorithms (A+)
CS2103T Software Engineering (A+)
CS2100 Computer Organisation (A+)
CS2030 Programming Methodology II (A+)
CS1231S Discrete Structures (A+)
MA1101R Linear Algebra I (A+)