Senior Quantization Research Engineer

New roles land in Following.

South KoreaPosted 3 months agofulltime

Market pay check

Matched to Computer systems and software professionals

Source: Ministry of Employment and Labor wage information system (wage.go.kr), wage distribution by occupation.

What you'd do

  • Research and productize quantization technology for the NetsPresso platform
  • Develop next-gen PTQ,QAT,and compression algorithms for production use
  • Design quantization strategies and frameworks for model architectures and target hardware

What they want

  • Hold degree in computer engineering,electronics,or related technical field
  • Have five plus years after bachelor's,three plus after master's,or PhD
  • Possess deep R&D experience in quantization,model compression,or DL optimization

Nice to have

  • Published quantization or compression papers at NeurIPS,ICML,CVPR,or ICLR
  • Shipped research results into commercial products and live customer services before
  • Advanced use of ExecuTorch,ONNX,TensorRT,or AIMET optimization libraries

Seoulstart's read

Visa tier (likely)
E-7 specialty / professional (likely)
Korean language
Korean requirement not stated
English friendliness
Medium: English JD, mixed-language workplace
Context
Seoul R&D at a domestic AI scale-up; Korean JD and multi-round process including a take-home assignment.

This is Seoulstart's analysis of the public job description, not the employer's stated policy. Verify visa sponsorship, language requirements, and remote allowances directly with the recruiter.

Original posting from Nota AI

๐Ÿ‘‹ ์šฐ๋ฆฌ ํŒ€์„ ์†Œ๊ฐœํ•ฉ๋‹ˆ๋‹ค.

NetsPresso Platform ํŒ€์€ Nota AI์˜ AI ๋ชจ๋ธ ๊ฒฝ๋Ÿ‰ํ™” ๋ฐ ์ตœ์ ํ™” ๊ธฐ์ˆ ์„ ๋ฐ”ํƒ•์œผ๋กœ, ์—ฐ๊ตฌยท๊ฐœ๋ฐœ๋œ ๊ธฐ์ˆ ์„ ์‹ค์ œ ์ œํ’ˆ์œผ๋กœ ๊ตฌํ˜„ํ•˜์—ฌ ์‚ฌ์šฉ์ž์—๊ฒŒ ์ œ๊ณตํ•˜๋Š” ํ•ต์‹ฌ ํ”Œ๋žซํผ๊ณผ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ์„ค๊ณ„ยท๊ตฌํ˜„ํ•˜๋Š” ์กฐ์ง์ž…๋‹ˆ๋‹ค.

Model Representation, Quantization, Graph Optimization, Model Engineering, SW Engineering ํŒŒํŠธ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ์œผ๋ฉฐ, ๊ทธ์ค‘ Quantization ํŒŒํŠธ๋Š” NetsPresso์˜ ํ•ต์‹ฌ ์ตœ์ ํ™” ๊ธฐ์ˆ ์ธ ์–‘์žํ™”๋ฅผ ์—ฐ๊ตฌํ•˜๊ณ , ์ž์ฒด ๊ธฐ์ˆ ์„ ์ œํ’ˆ์— ํƒ‘์žฌํ•˜์—ฌ ๋‹ค์–‘ํ•œ HW์—์„œ ๋”ฅ๋Ÿฌ๋‹ ๋ชจ๋ธ ์ถ”๋ก ์„ ๊ฐ€์†ํ™”ํ•ฉ๋‹ˆ๋‹ค.

  • ์–‘์žํ™”๋กœ ์ธํ•œ ์„ฑ๋Šฅ ์ €ํ•˜๋ฅผ ์ตœ์†Œํ™”ํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์—ฐ๊ตฌํ•˜๋ฉฐ
  • ๋‹ค์–‘ํ•œ HW ๋ฐ Backend์˜ ์ œ์•ฝ์‚ฌํ•ญ์— ๋งž๋Š” ์–‘์žํ™”๋ฅผ ์ง€์›ํ•˜๊ณ 
  • ์‹ค์ œ HW์—์„œ ๊ฐ€์† ๊ฐ€๋Šฅํ•œ ํ˜•ํƒœ๋กœ ๋ชจ๋ธ์„ ๋ณ€ํ™˜ํ•ฉ๋‹ˆ๋‹ค.

๐Ÿ“Œ ํ•ด๋‹น ํฌ์ง€์…˜์œผ๋กœ ํ•ฉ๋ฅ˜ํ•˜์‹ ๋‹ค๋ฉด

NetsPresso์˜ ํ•ต์‹ฌ ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•˜๋Š” ์–‘์žํ™” ๊ธฐ์ˆ ์„ ์—ฐ๊ตฌํ•˜๊ณ  ์ œํ’ˆํ™”ํ•ฉ๋‹ˆ๋‹ค. ์ตœ์‹  ์–‘์žํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์—ฐ๊ตฌํ•˜๊ณ , ๋‹ค์–‘ํ•œ ๋ชจ๋ธ๊ณผ HW์˜ ํŠน์„ฑ์— ๋งž๊ฒŒ ์ตœ์ ํ™”ํ•˜๋Š” ๋…ธํƒ€๋งŒ์˜ ๊ณ ์œ ํ•œ ์–‘์žํ™” ๊ธฐ๋ฒ•์„ ์„ค๊ณ„ํ•ฉ๋‹ˆ๋‹ค. On-device AI๋ฅผ ์œ„ํ•œ ๋‹ค์–‘ํ•œ ์ตœ์ ํ™” ๊ธฐ์ˆ ๊ณผ ์ตœ์‹  ๋ชจ๋ธ์„์„ ๊ฒฝํ—˜ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

โœ… ์ฃผ์š” ์—…๋ฌด

  • Quantization ๊ธฐ์ˆ  ์—ฐ๊ตฌ ๋ฐ ์ œํ’ˆํ™”
  • ์ฐจ์„ธ๋Œ€ Post-Training Quantization(PTQ), Quantization-Aware Training(QAT), Compression ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์—ฐ๊ตฌ ๋ฐ ๊ฐœ๋ฐœ
  • ๋ชจ๋ธ ์•„ํ‚คํ…์ฒ˜ ๋ฐ ํƒ€๊ฒŸ HW ํŠน์„ฑ์„ ๊ณ ๋ คํ•œ Quantization ์ „๋žต ์ˆ˜๋ฆฝ ๋ฐ ํ”„๋ ˆ์ž„์›Œํฌ ์„ค๊ณ„
  • Generative AI(LLM, VLM, Diffusion ๋“ฑ), Computer Vision(Classification, Detection, Segmentation ๋“ฑ) ๋ฐ ๋‹ค์–‘ํ•œ AI ๋ชจ๋ธ์— ๋Œ€ํ•œ ์ตœ์ ํ™” ๋ฐฉ๋ฒ•๋ก  ์—ฐ๊ตฌ
  • ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์˜ ๋…ผ๋ฌธ ๋ฐœํ‘œ ๋ฐ ํŠนํ—ˆ ์ถœ์›์„ ํ†ตํ•œ ๊ธฐ์ˆ  ๋ฆฌ๋”์‹ญ ํ™•๋ณด
  • On-device AI ๋ชจ๋ธ ์ตœ์ ํ™”
  • ๋‹ค์–‘ํ•œ On-device HW๋กœ์˜ ๋ฐฐํฌ๋ฅผ ์œ„ํ•œ quantization ํŒŒ์ดํ”„๋ผ์ธ ์„ค๊ณ„ ๋ฐ ๊ณ ๋„ํ™”
  • AI ๋ชจ๋ธ ์ตœ์ ํ™” ํ”„๋กœ์ ํŠธ ์ˆ˜ํ–‰
  • Quantization์œผ๋กœ ์ธํ•œ ์ •ํ™•๋„ ์ €ํ•˜ ์›์ธ ๋ถ„์„ ๋ฐ ์ฒด๊ณ„์ ์ธ ํ•ด๊ฒฐ ๋ฐฉ์•ˆ ์ˆ˜๋ฆฝ

โœ… ์ž๊ฒฉ์š”๊ฑด

  • ์ปดํ“จํ„ฐ๊ณตํ•™, ์ „์ž๊ณตํ•™ ๋“ฑ ๊ด€๋ จ ์ „๊ณต ํ•™์œ„ ์†Œ์ง€์ž
  • ํ•™์‚ฌ ํ›„ 5๋…„ ์ด์ƒ, ์„์‚ฌ ํ›„ 3๋…„ ์ด์ƒ, ๋ฐ•์‚ฌ ์กธ์—… ๋ฐ ์กธ์—… ์˜ˆ์ •์ž
  • Quantization, ๋ชจ๋ธ ์••์ถ•, ๋˜๋Š” ๋”ฅ๋Ÿฌ๋‹ ์ตœ์ ํ™” ๋ถ„์•ผ์—์„œ์˜ ์‹ฌ๋„ ์žˆ๋Š” ์—ฐ๊ตฌ/๊ฐœ๋ฐœ ๊ฒฝํ—˜
  • PyTorch, ExecuTorch, ONNX ๊ธฐ๋ฐ˜ ๋”ฅ๋Ÿฌ๋‹ ๋ชจ๋ธ ์ตœ์ ํ™”์— ๋Œ€ํ•œ ๊นŠ์€ ์ดํ•ด
  • ์†Œ๊ทœ๋ชจ ์ด์ƒ์˜ ํ”„๋กœ์ ํŠธ ๋ฆฌ๋“œ ๋ฐ ๊ด€๋ฆฌ ๊ฒฝํ—˜
  • ๊ธฐ์ˆ  ๋ฌธ์„œ ์ž‘์„ฑ ๋ฐ ์˜์–ด ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜ ๋Šฅ๋ ฅ
  • ํ•ด์™ธ ์—ฌํ–‰์— ๊ฒฐ๊ฒฉ ์‚ฌ์œ ๊ฐ€ ์—†๋Š” ๋ถ„

โœ… ์šฐ๋Œ€์‚ฌํ•ญ

  • Quantization, ๋ชจ๋ธ ์••์ถ•, ์ปค๋„ ์ตœ์ ํ™” ๊ด€๋ จ top-tier ํ•™ํšŒ(NeurIPS, ICML, CVPR, ICLR ๋“ฑ) ๋…ผ๋ฌธ ๋ฐœํ‘œ ๊ฒฝํ—˜
  • ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์˜ ์ œํ’ˆํ™” ๋ฐ ์ƒ์šฉ ์„œ๋น„์Šค ์ ์šฉ ๊ฒฝํ—˜
  • ExecuTorch, ONNX, TensorRT, AIMET ๋“ฑ ์ตœ์ ํ™” ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ ์‹ฌํ™” ํ™œ์šฉ ๊ฒฝํ—˜
  • Low-bit ๋ชจ๋ธ์˜ Embedded Device ํฌํŒ… ๋ฐ ์„ฑ๋Šฅ ํŠœ๋‹ ๊ฒฝํ—˜
  • vLLM, SGLang, llama.cpp ๋“ฑ LLM Inference Framework์— ๋Œ€ํ•œ ๊ฒฝํ—˜
  • ์˜คํ”ˆ์†Œ์Šค ํ”„๋กœ์ ํŠธ ๊ธฐ์—ฌ ๋˜๋Š” ์œ ์ง€๋ณด์ˆ˜ ๊ฒฝํ—˜
  • ๋ฐ•์‚ฌ ํ•™์œ„ ์†Œ์ง€์ž

โœ… ์ฑ„์šฉ ์ ˆ์ฐจ

  • ์„œ๋ฅ˜ ๊ฒ€ํ†  โ†’ 1์ฐจ ์ธํ„ฐ๋ทฐ โ†’ ์‚ฌ์ „ ๊ณผ์ œ โ†’ 2์ฐจ ์ธํ„ฐ๋ทฐ โ†’ 3์ฐจ ์ธํ„ฐ๋ทฐ โ†’ ์ฒ˜์šฐ ํ˜‘์˜ โ†’ ์ตœ์ข… ์„ ๋ฐœ
  • (์ ˆ์ฐจ ๊ฐ„์— ์ถ”๊ฐ€์ ์ธ ๊ณผ์ œ๊ฐ€ ์žˆ์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.)

๐Ÿค“ ํŒ€์˜ ๋ฉ”์„ธ์ง€

์ƒˆ๋กœ์šด ๊ธฐ์ˆ ์— ๋Œ€ํ•œ ๋†’์€ ๊ด€์‹ฌ๊ณผ ์•„์ด๋””์–ด๋ฅผ ์‹ค์ œ๋กœ ๊ตฌํ˜„ํ•ด๋‚ด๋Š” ์‹คํ–‰๋ ฅ์ด ์ค‘์š”ํ•ฉ๋‹ˆ๋‹ค. ๋‹จ์ˆœํ•œ ์—ฐ๊ตฌ์— ๊ทธ์น˜์ง€ ์•Š๊ณ , NetsPresso ์„œ๋น„์Šค์™€ ์ง์ ‘ ์—ฐ๊ฒฐ๋˜๋Š” ๋…์ž์ ์ธ ์–‘์žํ™” ๊ธฐ์ˆ ์„ ๊ฐœ๋ฐœํ•˜๋Š” ํฌ์ง€์…˜์ž…๋‹ˆ๋‹ค. ๊ฐ ๋ชจ๋“ˆ์ด ์œ ๊ธฐ์ ์œผ๋กœ ์—ฐ๊ฒฐ๋˜์–ด ์žˆ๋Š” ๋งŒํผ, ํŒ€ ๋‚ด ํ™œ๋ฐœํ•œ ์†Œํ†ต๊ณผ ์ฃผ๋„์ ์ธ ํƒœ๋„๋ฅผ ๋ฌด์—‡๋ณด๋‹ค ์ค‘์š”ํ•˜๊ฒŒ ์ƒ๊ฐํ•ฉ๋‹ˆ๋‹ค. ๋ณต์žกํ•œ ๊ธฐ์ˆ ์  ๋ฌธ์ œ๋ฅผ ๊นŠ์ด ์žˆ๊ฒŒ ํŒŒ๊ณ ๋“ค๊ณ , ํ˜‘์—…์„ ํ†ตํ•ด ํ•จ๊ป˜ ์„ฑ์žฅํ•˜๊ณ  ์‹ถ์€ ๋ถ„์ด๋ผ๋ฉด ์ด ํŒ€์—์„œ ๋ถ„๋ช… ์ข‹์€ ์„ฑ๊ณผ๋ฅผ ๋‚ผ ์ˆ˜ ์žˆ์„ ๊ฒƒ์ž…๋‹ˆ๋‹ค.

์ง€์› ์ „, ํ™•์ธํ•ด์ฃผ์„ธ์š”! ๐Ÿ‘€

  • ํ•ด๋‹น ๊ณต๊ณ ๋Š” ์ƒ์‹œ ์ฑ„์šฉ์œผ๋กœ, ์ฑ„์šฉ ์™„๋ฃŒ ์‹œ ์กฐ๊ธฐ ๋งˆ๊ฐ๋  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
  • ์ด๋ ฅ์„œ ๋‚ด ์—ฐ๋ด‰ ์ •๋ณด ๋“ฑ ๋ฏผ๊ฐํ•œ ๊ฐœ์ธ ์ •๋ณด๊ฐ€ ๊ธฐ์žฌ๋˜์–ด ์žˆ๋‹ค๋ฉด, ํ•ด๋‹น ์„œ๋ฅ˜๋Š” ๊ฒ€ํ† ๋˜์ง€ ์•Š์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
  • ์ œ์ถœํ•ด ์ฃผ์‹  ๋‚ด์šฉ ์ค‘ ํ—ˆ์œ„ ์‚ฌ์‹ค์ด ์žˆ์„ ๊ฒฝ์šฐ ์ฑ„์šฉ์ด ์ทจ์†Œ๋  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
  • ์ฑ„์šฉ ์ „ ๋ ˆํผ๋Ÿฐ์Šค ์ฒดํฌ๊ฐ€ ์žˆ์Œ์„ ์•Œ๋ ค๋“œ๋ฆฝ๋‹ˆ๋‹ค.
  • ์ตœ์ข… ์ธํ„ฐ๋ทฐ ํ•ฉ๊ฒฉ ์‹œ ๋ณ„๋„๋กœ ์ฒ˜์šฐ๋ฅผ ํ˜‘์˜ํ•ฉ๋‹ˆ๋‹ค.
  • ์ž…์‚ฌ ํ›„ ์‹œ์šฉ๊ธฐ๊ฐ„์ด ์žˆ์œผ๋ฉฐ, ์ด์— ๋Œ€ํ•œ ์ฒ˜์šฐ๋Š” ์ฐจ๋“ฑ์„ ๋‘์ง€ ์•Š์Œ์„ ์•Œ๋ ค๋“œ๋ฆฝ๋‹ˆ๋‹ค.
  • ๊ตญ๊ฐ€๋ณดํ›ˆ๋Œ€์ƒ์ž ๋ฐ ์žฅ์• ์ธ์€ ๊ด€๋ จ ๋ฒ•๊ทœ์— ์˜๊ฑฐํ•˜์—ฌ ์šฐ๋Œ€ํ•ฉ๋‹ˆ๋‹ค.
  • ์žฅ์• ์ธ ๊ณ ์šฉ ์ด‰์ง„์„ ์œ„ํ•œ ํ–‰์ •์  ์ ˆ์ฐจ ํ™•์ธ์ด ํ•„์š”ํ•œ ๊ฒฝ์šฐ, ์žฅ์• ์ธ ๋“ฑ๋ก์ฆ ์‚ฌ๋ณธ์„ ๊ธฐํƒ€ ์„œ๋ฅ˜ ๋ž€์— ์„ ํƒ์ ์œผ๋กœ ์ œ์ถœํ•˜์‹ค ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ œ์ถœ ์—ฌ๋ถ€๋Š” ์ „ํ˜• ํ‰๊ฐ€์— ์–ด๋– ํ•œ ์˜ํ–ฅ๋„ ๋ฏธ์น˜์ง€ ์•Š์Šต๋‹ˆ๋‹ค.
  • R&D ๋ชจ๋“  ํฌ์ง€์…˜์—์„œ ์ „๋ฌธ์—ฐ๊ตฌ์š”์›, ์‚ฐ์—…๊ธฐ๋Šฅ์š”์›์œผ๋กœ ๊ทผ๋ฌด๊ฐ€ ๊ฐ€๋Šฅํ•ฉ๋‹ˆ๋‹ค. (๋‹จ, ์‚ฐ์—…๊ธฐ๋Šฅ์š”์› ํ˜„์—ญ ์‹ ๊ทœ ํŽธ์ž…์˜ ๊ฒฝ์šฐ ๋ถˆ๊ฐ€๋Šฅ)

๐Ÿ”Ž ์ฝ์–ด๋ณด๋ฉด ๋„์›€ ๋˜๋Š” ๊ด€๋ จ ์ž๋ฃŒ

  • ๋…ธํƒ€์˜ ๊ทธ๋ผ์šด๋“œ ๋ฃฐ, LP๋ฅผ ์†Œ๊ฐœํ•ฉ๋‹ˆ๋‹ค
  • ๋…ธํƒ€๋Š” ํ•ต์‹ฌ ๊ฐ€์น˜(CV)๋ฅผ ์–ด๋–ป๊ฒŒ ์‹คํ–‰ํ• ๊นŒ?
  • ๋…ธํƒ€์˜ ๋‚˜์นจ๋ฐ˜, ์กฐ์ง๋ฌธํ™”
  • ๊ธฐ์ˆ  ์ด์•ผ๊ธฐ ์—†์ด AI ํšŒ์‚ฌ๋ฅผ ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ๋‚˜์š”?

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