Software Engineer, Kernel Programming

FuriosaAI · Application · Seoul, South Korea
공고 번호 4005375201

NPU/GPU에서 실행되는 저수준 kernel과 AI 연산 코드 구현.

학사 명시 · 도전 지원

확인할 점: XPU 대상 low-level systems programming 필수 경험이 확인되지 않음.

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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 bring hands-on PyTorch training, GPU workload operation, and reference-based model evaluation.

I developed business-card extraction that reached 94% accuracy across seven core fields. I applied teacher-student knowledge distillation and LoRA fine-tuning; separate difficult-case evaluation improved from 30.2% to 65.3% through further training and output correction.

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 model-side implementation and evaluation experience to kernel development. Training task-specific models and operating GPU workloads has given me a practical understanding of model operations, supported inputs, and the need to check outputs alongside performance. At FuriosaAI, I can contribute realistic model workloads and numerical reference checks while developing accelerator-specific implementation skills. I would evaluate each operation against explicit input ranges and correctness criteria before comparing performance.

Cover Letter

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Dear FuriosaAI Hiring Team,

I am applying for the Software Engineer, Kernel Programming position (Job ID 4005375201). I bring model-side implementation and evaluation experience to kernel development. Training task-specific models and operating GPU workloads has given me a practical understanding of model operations, supported inputs, and the need to check outputs alongside performance. 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 developed business-card information extraction used in TieMing, reaching 94% accuracy across seven core fields in a workflow covering approximately 5,000 cards. To address correct text being assigned to the wrong fields, I trained a smaller student model through knowledge distillation and low-rank adaptation (LoRA). I separately targeted difficult cases: follow-up training and selective regeneration of malformed outputs raised field-value accuracy from 30.2% to 65.3%. The work involved choosing the training method, diagnosing output errors, and implementing the changes in the extraction workflow.

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 realistic model workloads and numerical reference checks while developing accelerator-specific implementation skills. I would evaluate each operation against explicit input ranges and correctness criteria before comparing performance.

Sincerely,
Jeonghun Kim