# Hayden Kim

> Software Engineer at Waymo | CMU MSCS '22

Location: San Francisco Bay Area, United States
Profile: https://flows.cv/haydenkim

I am interested in building large-scale distributed systems, and designing artificial intelligence models and inference pipelines.

Some examples of my previous experience include:

- Created a workload management system at NVIDIA handling 65.3% of the workload, and a license prediction system with neural networks with a mean absolute percentage error as low as 0.39%
- Developed software for embedding a neural network inference pipeline into an existing medical imaging system for 4x faster MRI scans
- Published multiple papers on multimodal learning (computer vision and natural language processing) at top-tier international conferences such as ICCV and AAAI

I have also contributed to open-source projects such as TensorFlow and Keras.

## Work Experience
### Software Engineer @ Waymo
Jan 2023 – Present | Mountain View, California, United States
Formerly the Google Self-Driving Car Project

### Teaching Assistant @ Carnegie Mellon University
Jan 2022 – Jan 2022 | Pittsburgh, Pennsylvania, United States

### Deep Learning Infrastructure Engineer Intern @ NVIDIA
Jan 2022 – Jan 2022 | Santa Clara, California, United States
- Developed a workload management/scheduling system for allocating resources, handling up to 65.3% of the company workload
- Implemented the system API gateway using FastAPI and deployed using Nomad, passing stress tests with over 20k requests
- Designed to handle over 40k queries per minute using ElasticSearch, MySQL, Redis and RabbitMQ, and built a web front-end for visualization
- Built a license usage prediction system with an ensemble model of XGBoost regressors and LSTMs with a periodic accuracy monitor, mean absolute error as low as 0.39%; querying/posting data from/to Graphite and ElasticSearch, served using Nomad

### Teaching Assistant @ Carnegie Mellon University
Jan 2022 – Jan 2022 | Pittsburgh, Pennsylvania, United States
10-301/601 Introduction to Machine Learning
- Authored a 44-page write-up on matrix calculus with exercises and full solutions, selected as one of the recommended extra readings for the course
- Participated actively in Piazza Q&A board with 1,309 total contributions; highest among all CMU MLD TAs in history
- Hosted office hours on topics including neural network, reinforcement learning, logistic regression, recommender system and Bayesian network
- Wrote/graded homework and exam questions on reinforcement learning and logistic regression
- Provided starter code and set up Gradescope autograder for programming assignments

### Researcher @ Seoul National University
Jan 2020 – Jan 2021 | Seoul, South Korea
Vision & Learning Lab
- Achieved up to a 32.9% recall score improvement in interactive image retrieval by devising a neural network with cycle consistency
- Designed a novel neural network for viewpoint-agnostic change captioning and attained an improvement of up to 15 points in common natural language processing metrics such as CIDEr
- Published papers at AAAI 2021 and ICCV 2021, and gave an hour long lab presentation on recent image retrieval works
- Collaborated with Hyundai Motor Company and completed funded research projects (value of approximately 240k USD)
- Implemented neural networks with PyTorch, and created a dataset for change captioning under viewpoint changes using Blender

### Teaching Assistant @ Seoul National University
Jan 2019 – Jan 2019 | Seoul, South Korea
4190.307 Operating Systems
- Gave presentations on Linux Development Environment and Debugging Tips and class projects
- Reviewed the design of all class projects and managed the class discussion board, answered over 55% of the questions
- Ported all class projects to a newer Linux environment (Samsung ARTIK 10 with Linux 3.10.93 ARM to Raspberry Pi 3 with Linux 4.14.67 AArch64) and set up QEMU

Class Projects:
- System call that prints all processes currently running in DFS order
- Device orientation based reader-writer lock to kernel
- Weighted round-robin scheduler for multiprocessor systems
- Geo-tagged file system with GPS based permissions

### Undergraduate Researcher @ Seoul National University
Jan 2018 – Jan 2018 | Seoul, South Korea
Laboratory for Imaging Science and Technology
- Designed a deep learning based quantitative susceptibility mapping (QSM) pipeline for MRI and implemented it with TensorFlow
- Produced results almost identical to gold standard (SSIM 0.90-0.99) with over two orders of magnitude of speedup; work published at ISMRM 2019


## Education
### Master of Science - MS in Computer Science
Carnegie Mellon University

### Bachelor of Science - BS in Electrical and Computer Engineering
Seoul National University


## Contact & Social
- LinkedIn: https://linkedin.com/in/hkim24

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Source: https://flows.cv/haydenkim
JSON Resume: https://flows.cv/haydenkim/resume.json
Last updated: 2026-03-29