Software Engineer, Kernel Programming Model

FuriosaAI · Software · Seoul, South Korea
공고 번호 4005790201

PyTorch 확장·가상 ISA·kernel 개발 도구와 프로그래밍 인터페이스 설계.

학사 명시 · 도전 지원

확인할 점: Rust/C++/Go 시스템 개발, ISA·SIMD·메모리 계층의 직접 개발 근거 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 make model inputs, output expectations, and execution state explicit in working APIs.

I improved normalized database-result agreement from 26.6% to 76.6% on a 64-question internal CRM development benchmark through teacher-student training and LoRA. I also verified and documented the schema input required to reproduce the model's behavior in serving.

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 bring experience designing model input contracts and stateful processing APIs, where implicit assumptions can change execution even when the model is unchanged. I make those assumptions explicit and validate them through runnable examples. At FuriosaAI, I can contribute developer-facing examples and validation for the kernel programming model. I would connect interface behavior to execution requirements, using representative operations to make supported inputs and results clear.

Cover Letter

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Dear FuriosaAI Hiring Team,

I am applying for the Software Engineer, Kernel Programming Model position (Job ID 4005790201). I bring experience designing model input contracts and stateful processing APIs, where implicit assumptions can change execution even when the model is unchanged. I make those assumptions explicit and validate them through runnable examples. 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 improved a four-billion-parameter Text-to-SQL model from 26.6% to 76.6% normalized database-result agreement on a 64-question internal CRM development benchmark, using teacher-student training and rank-16 LoRA. The model matched 49 of 64 reference results, compared with 17 for the base model; scoring ignores row order and normalizes numbers and dates. I also verified the serving interface: missing schema input caused invented table names despite unchanged weights. I documented the required schema inputs alongside the adapter so the trained behavior could be reproduced in serving.

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.

At FuriosaAI, I can contribute developer-facing examples and validation for the kernel programming model. I would connect interface behavior to execution requirements, using representative operations to make supported inputs and results clear.

Sincerely,
Jeonghun Kim