👋 Hi There, I'm

Dawei Liu

About Me

I’m Dawei Liu, a graduate student in Computer and Information Science (CIS) at the University of Pennsylvania, expected to graduate in May 2026. I hold a Bachelor’s degree in Software Engineering from Northeastern University (NEU).

Currently, I’m an SDE Intern at TikTok, working on the Commerce Ads team, where I focus on Ads delivery, recommendation infrastructure and performance optimization for large-scale ads delivery systems. Previously, I interned at Amazon and JD.com, where I contributed to the distributed systems observability and AI platforms. I was honored to receive 2026 full-time return offers from both TikTok and Amazon for my internship performance.

I enjoy working at the intersection of AI infrastructure and systems performance, and I’m passionate about solving large-scale engineering problems that demand both scalability and efficiency.

🎓 Education

🔹 University of Pennsylvania – M.S.E in Computer and Information Science

Aug 2024 – May 2026

GPA: 3.88 / 4.00
Awards:
- Hagan International Scholarship

🔹 Northeastern University – B.E. in Software Engineering

Sep 2020 – Jul 2024

GPA: 3.95 / 4.00
Awards:
- National Scholarship
- Merit-based Scholarship
- Outstanding Student Leader
- Outstanding Graduate

💼 Professional Experience

🔹 TikTok – SWE Intern, Commerce Ads

May 2025 - Dec 2025

At TikTok, I focused on building scalable and fault-tolerant recommendation infrastructure for Commerce Ads. I designed and optimized systems to support high-throughput, low-latency Ads delivery, including a debugging and verfication framework for new ad formats, cache restructuring that improved stability under heavy traffic, and real-time Flink features for ranking and creative selection. My work directly enhanced the platform’s resilience, efficiency, and rollout velocity at global scale.

Built Modular Preview Flow, a framework enabling stage-level entity injection (Ad group, Creative, SPU) across all delivery funnel stages with unified filtering log, improving debugging and verification efficiency for new ad formats and region rollout.
Enhanced product handler stability by redesigning cache mechanism and applying async batch fetching with Folly Future, mitigating 80% of failure spikes in high-traffic scenarios. Migrated online product value call to offline, reducing 13% latency.
Merged default and retrieval clusters of product handler by improving stability, reducing latency and extracting filter logic.
Developed a realtime feature for PSA Carousel Image Selection with posterior impression/click events gourped by image URI.
Integrated offline AIGC pipeline for high-GMV products, backfilling images into TBase and updating index service with Flink.

🔹 Amazon – SDE Intern, Global Mile

Jun 2024 - Sep 2024

At Amazon, I developed a custom Java Agent to extend OpenTelemetry’s tracing, enabling end-to-end observability across distributed microservices and Lambda environments. I built full-stack tools for trace visualization and implemented a Loosely Link module that logically connected services without direct invocations. By improving traceability, reliability, and developer tooling, I enhanced the debugging and monitoring experience for complex distributed systems.

Developed a Java Agent that extends OpenTelemetry. Leveraged ByteBuddy to enhance methods annotated with @WithSpan, @Input, and @Output, enabling automatic tracing and payload collection.
Implemented frontend UI and backend APIs, with filters, aggregation, fuzzy search, pagination, and trace details visualizations including tree plot, table, timeline, end-to-end hyper process and span payloads (inputs, outputs and errors).
Enhanced end-to-end traceability with Loosely Link module that dynamically connects relevant services using business IDs and timestamps, enabling logical linkage even without direct calls (e.g., async MQ or partial service onboarding).
Utilized reflection to detect Lambda environments, and employed a lightweight Kinesis SDK. Implemented connection pooling, retry and flush sync mechanisms to ensure reliable data streaming within Lambda size constraints.

🔹 JD – SDE Intern, Algorithm Tools

Jul 2023 - Oct 2023

At JD.com, I worked on platform engineering for internal AI tooling. I redesigned a resource management service using Kubernetes' Informer + observer pattern, reducing start-up time by 20x. I introduced GitOps + Argo Workflows for cloud-native CI/CD, built Helm charts for privatized deployments, and improved code modularity for activity page generation using AIGC pipelines. My work enabled faster and more maintainable delivery of algorithmic components.

Refactored the resource management service with ConfigMap for automated and configurable resource-splitting solutions.
Utilized the Kubernetes Informer mechanism, observer pattern, async processing, row locking, and discard policies, to improve platform resource recalculation efficiency and achieve a 20x faster service startup speed.
Enhanced the CI/CD toolchain with cloud-native CI workflows using Argo Workflows and GitOps to trigger Argo CD.
Developed Helm charts to support robust on-premise and client-tailored deployments, enabling flexible private cloud delivery.
Refactored the campaign page generation service with the strategy pattern, improving modularity and reusability.
Designed generation pipelines integrated with AIGC services for automated creation of campaign page sections.

🛠️ Tech Stack

Language: Java, C/C++, JavaScript/TypeScript, HTML/CSS, Go, Python, Swift, SQL
Backend: Spring Framework, Guice, Coral, Thrift, Protobuf, MyBatis, MySQL, Redis, RabbitMQ, ElasticSearch, OpenTelemetry
Frontend: React, Vue, Vite, ECharts, AWS UI, Arco Design; iOS: Swift, SwiftUI, ARKit
AI/ML: PyTorch, LLM (Transformer, RLHF, Token Pruning, CLIP), RecSys (ItemCF, Two-/Three-Tower, MTL)
Graphics: OpenGL, GLSL, Unity, Qt, Maya API (Plugin Development), Auto-Rigging (RigNet)
DevOps: Unix/Linux, Docker, Kubernetes, Helm, AWS (DynamoDB, Kinesis, S3, Load Balancer, CodePipeline, CDK)
Tools: Git, Vim, SSH, CI/CD, Shell, Markdown, LaTeX, Mermaid, VuePress

💬 Let’s Connect

Whether you're into recommendation systems, AI infra, or excellent engineering, I'd love to connect and chat. Thanks for stopping by!