Senior Software Engineer, Runtime

FuriosaAI · Software · Seoul, South Korea
공고 번호 4005789201

NPU 실행을 받치는 저수준 runtime·메모리·시스템 코드 개발.

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확인할 점: 5년 Rust/C/C++ 시스템 프로그래밍 또는 동등 역량과 DMA/MMIO 등 필수 경험 미확인.

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Career Summary

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I am an ML engineer and AI Researcher at ISPARK R&D Center responsible for TieMing's core AI features, from model training to inference APIs and deployment. I bring a multimodal research background—five publications, four as first author—and turn experimental results into working products. I design asynchronous processing around explicit state, resource admission, and restart recovery.

I reduced server processing time by 36.9% on a 30-minute recording through speech-region batching. I also redesigned long-audio processing as recoverable asynchronous jobs, separating completion and recovery from the HTTP request lifetime.

I build and operate vLLM and FastAPI model services with Docker deployment and service/GPU monitoring. I ran distributed training on two RTX 4090 GPUs with checkpoint recovery and now also use two A100 GPUs through NIPA's support program.

I bring experience designing stateful processing where ordering, resource ownership, and recovery determine whether work completes correctly. In speech services, I handled late input, repeated completion, and interrupted jobs through explicit session and job state. At FuriosaAI, I can contribute disciplined state and recovery analysis to runtime development. I would trace ownership and transitions, reproduce ordering failures with controlled event sequences, and verify that changes preserve both completion and resource behavior.

Cover Letter

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Dear FuriosaAI Hiring Team,

I am applying for the Senior Software Engineer, Runtime position (Job ID 4005789201). I bring experience designing stateful processing where ordering, resource ownership, and recovery determine whether work completes correctly. In speech services, I handled late input, repeated completion, and interrupted jobs through explicit session and job state. Before ISPARK, I served one year as an information-systems NCO in the Republic of Korea Air Force (Dec 2021–Dec 2022), maintaining Linux servers through shell scripting and log analysis.

I redesigned long-audio processing and reduced server processing time by 36.9%, from 122.6 to 77.4 seconds on a 30-minute recording, using Silero voice activity detection and faster-whisper batching at batch size 8. The same workload had also exposed a delivery problem: the server finished after the synchronous HTTP request timed out. I separated live sessions from recoverable asynchronous jobs, with task polling and persisted progress. I treated recognition quality, execution time, and delivery as separate engineering problems, allowing each change to address a specific cause.

I take responsibility for model deployment as well as training, building vLLM and FastAPI inference services in Docker with Prometheus and Grafana monitoring. Watching service failures alongside GPU usage helps me distinguish application problems from resource constraints. I ran distributed training on two RTX 4090 GPUs with BF16 precision and checkpoint recovery, and now also use two A100 GPUs through a national computing-support program. I track experiments in Weights & Biases so model changes can be compared across runs.

At FuriosaAI, I can contribute disciplined state and recovery analysis to runtime development. I would trace ownership and transitions, reproduce ordering failures with controlled event sequences, and verify that changes preserve both completion and resource behavior.

Sincerely,
Jeonghun Kim