Vincent Huang

黄陈谆

Developer, Student, Musician

About Me

Hello there! I'm Vincent (Chenzhun) Huang, a grad student at Carnegie Mellon University at Language Technologies Institute under School of Computer Science. My academic journey began with a Bachelor's degree in Computer Science from McGill University.

My professional experiences have been diverse, and I have been exploring places in the past 4 years from 6 internships totalling 24 months. These experiences allowed me to work on projects varying from model performance optimization to developing user-centric software features. On top of that, I've gained hands-on experience in end-to-end development with tools like Spring WebFlux and Kubernetes.

Beyond academia and work, I find joy in working on personal projects such as search-engine development and building digital platforms for communities. If you want to connect, you can find me on LinkedIn or browse through my work on Github. Looking forward to some stimulating collaborations and discussions!

Apple Inc

Software Engineer Intern (2023-05 ~ 2023-08)

I joined as the first machine learning engineer on the team and work as a SWE and MLE

Major highlights of this internship:

- Service Facade: Lead the end-to-end development of a proxy layer with Spring WebFlux from scratch used by 4 teams while providing advanced customizable features (Rate Limiter, Circuit Breaker) for each of the upstream service.

- VoIP QoE: Research, design, and integrate Quality of Experience(QoE) metrics for call sessions with Machine Learning into Apple’s Retail Platform.

- Deployment and Monitoring: Integrate the application into Kubernetes cluster, set up ACL and security groups. Set up Splunk Stats Services with application.

Unity Technologies

Research Intern (2022-05 ~ 2022-08)

I worked with the Deeppose team in the Unity Labs to help address the pose estimation problem from inverse kinematics using the ProtoRes Network.

Major highlights of this internship:

- Knowledge Distillation and Model Architecture: Designed model architecture which, combined with knowledge distillation, achieving comparable performance to the original model proposed by Harvey et al with only 1/20 of size

- FPS Improvements: Supported 5 times as many frames a second as the original model by improving model inference time

- ML Engineering: Adapted Knowledge Distillation algorithm to motion synthesis tasks

Unity Technologies

Software Developer Intern (2021-05 ~ 2021-08)

I worked with a team of 5 to build and improve the Unity Package Manager

Major highlights of this internship:

- Provided user-facing, event-oriented download progression report for Unity packages

- Unified installing and caching pipeline for packages from different sources

- Established decentralized testing framework for the whole team while maintaining 100% code coverage

- Improved search reply query, which cut the client-side package search time

SSENSE

Full-Stack Developer Intern (2020-06 ~ 2020-08)

I worked with a newly launched team of 5 to build a full suite of internal, automated system to achieve scalable workflow and productivity gains for the SSENSE Studio.

Major highlights of this internship:

- Streamlined 10% of the merchandise upload, cutting the studio's work on inter-season products by 3 hours daily

- Converted a student project into an industry-level full-stack internal talent booking system in TypeScript and React with integrated continuous monitoring.

- Implemented authentication, ACL (Access Control List) to the talent booking tool.

Cerence Inc

Software Developer Intern (2019-09 ~ 2020-04)

As a Software Developer intern, I worked with a team of 25 people in building a dockerized, voice-driven AI for major automotive companies including Daimler, AUDI, BMW and Toyota, etc.

Major highlights of this internship:

- I designed, implemented and presented an automated error tracer scaled for 9 deployment environments for 3 different teams.

- I automated internal user namespace manager and significantly eased the onboarding process for new employees.

- I implemented middleware test report, which greatly accelerated defect tracking during maintenance