Machine Learning Engineer

Seoul, South Korea

Tridge

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์ด๋Ÿฌํ•œ ๋ชฉํ‘œ๋ฅผ ํ•จ๊ป˜ ์ด๋ฃจ๊ณ ์ž ํ•ฉ๋‹ˆ๋‹ค:

ํŠธ๋ฆฟ์ง€์˜ Machine Learning Engineer ๋Š” ์ฐฝ์˜์ ์ธ ์‚ฌ๊ณ ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๋จธ์‹ ๋Ÿฌ๋‹์„ ๋น„๋กฏํ•œ ๋ฐ์ดํ„ฐ ํ”„๋กœ์„ธ์‹ฑ ๊ธฐ์ˆ ์„ ํ™œ์šฉํ•˜์—ฌ ๋น„์ฆˆ๋‹ˆ์Šค๋ฅผ ํ˜์‹ ํ•˜๋Š” ์†”๋ฃจ์…˜์„ ๋งŒ๋“œ๋Š” ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•˜๋ฉฐ, ๋ฐ์ดํ„ฐ ์ด๋‹ˆ์…”ํ‹ฐ๋ธŒ์— ๋Œ€ํ•ด ์†Œํ”„ํŠธ์›จ์–ด ๊ฐœ๋ฐœ์ž, ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ์„ค๊ณ„์ž, ๋ฐ์ดํ„ฐ ๋ถ„์„๊ฐ€๋ฅผ ์ง€์›ํ•˜๊ณ  ์ง„ํ–‰ ์ค‘์ธ ํ”„๋กœ์ ํŠธ ์ „๋ฐ˜์— ๊ฑธ์ณ ์ตœ์ ์˜ ๋ฐ์ดํ„ฐ ์ „๋‹ฌ ์•„ํ‚คํ…์ฒ˜๊ฐ€ ์ผ๊ด€๋˜๋„๋ก ๋ณด์žฅํ•˜๋Š” ์ฑ…์ž„์„ ๊ฐ€์ง€๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค.

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์ด๋Ÿฌํ•œ ์—…๋ฌด๋ฅผ ์ฃผ๋„์ ์œผ๋กœ ์ˆ˜ํ–‰ํ•ฉ๋‹ˆ๋‹ค:

  • ๋น„์ฆˆ๋‹ˆ์Šค ์š”๊ตฌ ์‚ฌํ•ญ์— ๋ถ€ํ•ฉํ•˜๋Š” ๋จธ์‹ ๋Ÿฌ๋‹ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜ ๊ฐœ๋ฐœ ํ”„๋กœ์ ํŠธ๋ฅผ ๋ฆฌ๋“œํ•ฉ๋‹ˆ๋‹ค.
  • ๋ฐฉ๋Œ€ํ•œ ์–‘์˜ ๋ˆ„์  ๋ฐ์ดํ„ฐ๋ฅผ ๋ถ„์„ํ•˜์—ฌ ์˜ˆ์ธก ๊ฐ’์„ ๋„์ถœํ•ด๋ƒ…๋‹ˆ๋‹ค.
  • ์ƒˆ๋กœ์šด API๋ฅผ ๊ตฌ์ถ•ํ•˜์—ฌ ๋ฐ์ดํ„ฐ ๋ณผ๋ฅจ๊ณผ ๋ณต์žก์„ฑ์˜ ์ง€์†์ ์ธ ์ฆ๊ฐ€์— ๊ธฐ์—ฌํ•ฉ๋‹ˆ๋‹ค.
  • ๋ฐ์ดํ„ฐ ๋ณ€ํ™˜ ๋ฐ ์ •์ œ, ๊ธฐ๋Šฅ ๊ฐœ๋ฐœ, ๋ชจ๋ธ ๊ตฌํ˜„์— ๋Œ€ํ•œ ์ „๋ฐ˜์ ์ธ ์—…๋ฌด์— ์ฐธ์—ฌํ•ฉ๋‹ˆ๋‹ค.
  • ํ”„๋กœ์ ํŠธ ๋ฒ”์œ„ ์ง€์ •, ๋ฐ์ดํ„ฐ ์š”๊ตฌ ์‚ฌํ•ญ, ๋ชจ๋ธ๋ง ์ „๋žต ๋ฐ ๋ฐฐํฌ ์š”๊ตฌ์‚ฌํ•ญ ๋“ฑ์„ ํฌํ•จํ•œ ๋จธ์‹ ๋Ÿฌ๋‹ ์‹œ์Šคํ…œ์„ ์„ค๊ณ„ํ•ฉ๋‹ˆ๋‹ค.
  • ๋ฐฑ์—”๋“œ ์—”์ง€๋‹ˆ์–ด ๋ฐ ๋ฐ์ดํ„ฐ ์—”์ง€๋‹ˆ์–ด์™€ ๊ธด๋ฐ€ํ•˜๊ฒŒ ํ˜‘๋ ฅํ•˜์—ฌ ๋จธ์‹ ๋Ÿฌ๋‹ ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•ฉ๋‹ˆ๋‹ค.
  • ์ปค์Šคํ…€ ๋ฐ์ดํ„ฐ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฐœ๋ฐœํ•ฉ๋‹ˆ๋‹ค.
  • ์ฝ”๋“œ ๋ฆฌ๋ทฐ์— ์ฐธ์—ฌํ•˜์—ฌ ์ฝ”๋“œ ํ’ˆ์งˆ์„ ๋ณด์žฅํ•˜๊ณ  ๊ณต์œ ํ•จ์œผ๋กœ์จ ํŒ€์˜ ์‹ค๋ ฅ ํ–ฅ์ƒ์— ๊ธฐ์—ฌํ•ฉ๋‹ˆ๋‹ค.

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์ด๋Ÿฌํ•œ ์—ญ๋Ÿ‰๊ณผ ๊ฒฝํ—˜์ด ์žˆ๋Š” ๋ถ„๋“ค์„ ์ฐพ์Šต๋‹ˆ๋‹ค:

  • ์ปดํ“จํ„ฐ๊ณตํ•™ ๋˜๋Š” ๊ด€๋ จ ๋ถ„์•ผ์˜ ํ•™์‚ฌ ํ•™์œ„๋ฅผ ์ทจ๋“ํ•˜์‹  ๋ถ„
  • ๋ฐ์ดํ„ฐ ์‚ฌ์ด์–ธํ‹ฐ์ŠคํŠธ ๋˜๋Š” ๋จธ์‹  ๋Ÿฌ๋‹ ์—”์ง€๋‹ˆ์–ด๋กœ์„œ 3๋…„ ์ด์ƒ์˜ ๊ฒฝํ—˜์„ ๋ณด์œ ํ•˜์‹  ๋ถ„
  • ๊ตฌ์กฐํ™”๋˜์ง€ ์•Š์€ ๋น„์ •ํ˜• ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•œ ํƒ„ํƒ„ํ•œ ๋ถ„์„ ์Šคํ‚ฌ์„ ๋ณด์œ ํ•˜์‹  ๋ถ„ (๋ฐ์ดํ„ฐ ์ˆ˜์ง‘, ๋ถ„์„, ํ•ด์„ ๋ฐ ์‹œ๊ฐํ™” ํฌํ•จ)
  • ๋ฐ์ดํ„ฐ ๊ณผํ•™ ๊ฐœ๋…์— ์ต์ˆ™ํ•˜์‹  ๋ถ„
  • ๋จธ์‹  ๋Ÿฌ๋‹ ๊ธฐ์ˆ ์— ๋Œ€ํ•œ ์ง€์‹์„ ๋ณด์œ ํ•˜์‹  ๋ถ„
  • SQL, Python ์–ธ์–ด ์‚ฌ์šฉ์— ๋Šฅ์ˆ™ํ•˜์‹  ๋ถ„
  • Jupyter Notebook, TensorFlow, Pytorch ์‚ฌ์šฉ ๊ฒฝํ—˜์ด ์žˆ์œผ์‹  ๋ถ„ย 
  • ๋†’์€ ์—”์ง€๋‹ˆ์–ด๋ง ์—ญ๋Ÿ‰์œผ๋กœ ์„ฑ๊ณต์ ์ธ ๋จธ์‹ ๋Ÿฌ๋‹ ํ”„๋กœ์ ํŠธ๋ฅผ ๊ตฌ์ถ•ํ•  ์ˆ˜ ์žˆ๋Š” ๋ถ„

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์ด๋Ÿฌํ•œ ๊ฐ•์ ์ด ์žˆ๋‹ค๋ฉด ๋”์šฑ ์ข‹์Šต๋‹ˆ๋‹ค:

  • ๋น…๋ฐ์ดํ„ฐ ํˆด ๊ฒฝํ—˜์„ ๋ณด์œ ํ•˜์‹  ๋ถ„ (Hadoop, Spark, Kafka ๋“ฑ)
  • Postgres ๋ฐ HBASE, Apache Cassandra ๋“ฑ์„ ํฌํ•จํ•œ ๊ด€๊ณ„ํ˜• SQL ๋ฐ NoSQL ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ๊ฒฝํ—˜์„ ๋ณด์œ ํ•˜์‹  ๋ถ„
  • NoSQL ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ์‚ฌ์šฉ ๊ฒฝํ—˜์ด ์žˆ์œผ์‹  ๋ถ„
  • ๋จธ์‹  ๋Ÿฌ๋‹ ํ”„๋ ˆ์ž„์›Œํฌ ์‚ฌ์šฉ ๊ฒฝํ—˜์ด ์žˆ์œผ์‹  ๋ถ„
  • ๋ฐ์ดํ„ฐ ๋ชจ๋ธ, ๋ฐ์ดํ„ฐ ๋งˆ์ด๋‹ ๋ฐ ์„ธ๋ถ„ํ™” ๊ธฐ์ˆ ์— ๋Œ€ํ•œ ์ „๋ฌธ์„ฑ์„ ๊ฐ–์ถ”์‹  ๋ถ„ย 
  • SQL ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ์„ค๊ณ„์— ๋Œ€ํ•œ ์‹ค๋ฌด ๊ฒฝํ—˜์ด ์žˆ์œผ์‹  ๋ถ„
  • ๋ฐ์ดํ„ฐ ๋žญ๊ธ€๋ง(Data Wrangling) ๊ฒฝํ—˜์ด ์žˆ์œผ์‹  ๋ถ„
  • ์˜์–ด๋กœ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜ ํ•  ์ˆ˜ ์žˆ๋Š” ๋Šฅ๋ ฅ (๋น„์ฆˆ๋‹ˆ์Šค ๋ ˆ๋ฒจ์€ ํ•„์š”ํ•˜์ง€๋งŒ ์œ ์ฐฝํ•จ์€ ํ•„์š”ํ•˜์ง€ ์•Š์Œ)

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์ง€์› ์ „ ํ™•์ธ ๋ถ€ํƒ๋“œ๋ฆฝ๋‹ˆ๋‹ค:

  • ์ฑ„์šฉํ˜•ํƒœ: ์ •๊ทœ์ง(์ˆ˜์Šต๊ธฐ๊ฐ„ 3๊ฐœ์›”)
  • ๊ทผ๋ฌด์‹œ๊ฐ„: ์›” โ€“ ๊ธˆ, 09:00 โ€“ 18:00
  • ๊ทผ๋ฌด์œ„์น˜: ์„œ์šธํŠน๋ณ„์‹œ ์„œ์ดˆ๊ตฌ ๋ฐฉ๋ฐฐ๋กœ 226
  • ์ œ์ถœ์„œ๋ฅ˜: ์ด๋ ฅ์„œ(ํ•„์ˆ˜), ํฌํŠธํด๋ฆฌ์˜ค(ํ•„์ˆ˜)

* ์ œ์ถœ์–‘์‹ : ๊ตญ๋ฌธ ๋˜๋Š” ์˜๋ฌธ/์ž์œ ์–‘์‹/PDF
* ๊ธฐ๊ฐ„์ด ๋งŒ๋ฃŒ๋œ ์ž๊ฒฉ์ฆ์€ ํ‰๊ฐ€์— ๋ฐ˜์˜๋˜์ง€ ์•Š์œผ๋ฉฐ, ์ง€์›์„œ ๊ธฐ์žฌ์‚ฌํ•ญ ์ฆ๋น™์ด ๋ถˆ๊ฐ€๋Šฅํ•œ ๊ฒฝ์šฐ ์ฑ„์šฉ์ด ์ทจ์†Œ๋  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.ย 

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About TRIDGE:

"์ตœ๊ณ ์˜ ์ธ์žฌ๋“ค๊ณผ ์ตœ๊ณ ์˜ ๋ฌธ์ œ๋ฅผ ํ’€์–ด ์ตœ๋Œ€ ๋‹ค์ˆ˜์˜ ์‚ฌ๋žŒ๋“ค๊ณผ ๊ทธ ํ˜œํƒ์„ ๋‚˜๋ˆŒ ์ˆ˜ ์žˆ๋Š” ๊ณณ.

์šฐ๋ฆฌ๋Š” ๋†’์€ ์ด์ƒ์„ ๊ฐ€์ง„ ์ž๋“ค๊ณผ ์ผํ•˜๊ธฐ๋ฅผ ์›ํ•ฉ๋‹ˆ๋‹ค. Tridge์— ๋ชจ์ธ ์‚ฌ๋žŒ๋“ค์€ ๋‹จ์ˆœํžˆ โ€œ์ตœ๊ณ ์˜ ์ง์žฅโ€, โ€œ๋น ๋ฅด๊ฒŒ ์„ฑ์žฅํ•˜๋Š” ํšŒ์‚ฌโ€, โ€œ๋†’์€ ์ˆ˜์ค€์˜ ์ฃผ์ธ์˜์‹โ€์„ ์ถ”๊ตฌํ•ด์„œ ๋ชจ์ธ ์‚ฌ๋žŒ๋“ค์ด ์•„๋‹™๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒƒ๋“ค์€ ์–ด์ฉŒ๋ฉด How์— ํ•ด๋‹นํ•˜๋Š” ๊ฒƒ์œผ๋กœ ์ตœ์„ ์˜ ๊ฒฐ๊ณผ๋ฅผ ๋‚ด๊ธฐ ์œ„ํ•ด ๋‹น์—ฐํžˆ ํ•„์š”ํ•œ ๊ฒƒ๋“ค์ด๊ฒ ์ง€์š”. ์šฐ๋ฆฌ๊ฐ€ ์ง„์ • ์ถ”๊ตฌํ•˜๋Š” ๊ฒƒ์€, ์„ธ์ƒ์— ๋„๋ฆฌ ํผ์ ธ ์žˆ๋Š” ๋ฌธ์ œ์ด์ง€๋งŒ ์‚ฌ๋žŒ๋“ค์ด ๋‹น์—ฐํ•˜๊ฒŒ ์ƒ๊ฐํ•˜๋Š”, โ€œ์ •๋ณด ๋น„๋Œ€์นญโ€, โ€œ ์‹œ์žฅ ๋ถˆ๊ท ํ˜•โ€, โ€œ์†Œ๋“์˜ ๋ถˆ๊ท ํ˜•โ€, โ€œ์—์ด์ „ํŠธ ๋ฌธ์ œโ€ ๋“ฑ์„ ๊ฐ™์ด ํ•ด๊ฒฐํ•˜๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์—ญ์‚ฌ์ ์œผ๋กœ ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋“ค์„ ํ’€๊ธฐ ์œ„ํ•œ ์ˆ˜๋งŽ์€ ์‹œ๋„๋“ค์ด ์žˆ์—ˆ์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์—ฌ์ „ํžˆ ํ•ด๊ฒฐํ•˜์ง€ ๋ชปํ•œ ๋ฏธ์™„์˜ ์ˆ™์ œ๋กœ ๋‚จ์•„ ์žˆ๋Š” ๊ฒƒ์ด ์‚ฌ์‹ค์ž…๋‹ˆ๋‹ค. ํŒ€ ํŠธ๋ฆฟ์ง€๋Š” ์ด๋Ÿฌํ•œ ์‚ฌ์‹ค์ธ์‹์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ, ๊ฐ€์žฅ ์ง€์†๊ฐ€๋Šฅํ•˜๊ณ , ๊ฐ€์žฅ ๊ฒฝ์ œ์ ์ด๋ฉด์„œ ํ•ฉ๋ฆฌ์ ์ธ ๋ฐฉ๋ฒ•์œผ๋กœ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜์—ฌ, ์ „์„ธ๊ณ„ ๋งŽ์€ ์‚ฌ๋žŒ๋“ค์ด ๊ทธ ํ˜œํƒ์„ ๊ณต์œ ํ•˜๊ธฐ๋ฅผ ๋ฐ”๋ผ๋Š” ์‚ฌ๋žŒ๋“ค์ž…๋‹ˆ๋‹ค. ํ•ด๊ฒฐํ•˜๊ณ ์ž ํ•˜๋Š” ๋ฌธ์ œ๊ฐ€ ํฌ๊ธฐ ๋•Œ๋ฌธ์— ์šฐ๋ฆฌ๋Š” ์ตœ๊ณ ์˜ ์ธ์žฌ๋ฅผ ์ฐพ์„ ์ˆ˜๋ฐ–์— ์—†์œผ๋ฉฐ, ๊ฐ€์žฅ ํšจ์œจ์ ์œผ๋กœ ์ผํ•ด์•ผ ํ•˜๋ฉฐ, ๊ฐ์ž ๋งก์€ ๋ถ€๋ถ„์— ๋Œ€ํ•ด ์ฑ…์ž„๊ฐ์„ ์ตœ๋Œ€ํ•œ ๋ฐœํœ˜ํ•˜๊ธฐ ์œ„ํ•ด ์ฃผ์ธ์˜์‹์ด ์ค‘์š”ํ•  ์ˆ˜๋ฐ–์— ์—†์Šต๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐฉ๋ฒ•๋ก ์€ ์„ ํƒ์ด ์•„๋‹ˆ๋ผ ํ•„์ˆ˜์ž…๋‹ˆ๋‹ค.

๋†’์€ ์ˆ˜์ค€์˜ ๊ต์œก์„ ๋ฐ›๊ณ , ๋†’์€ ์ด์ƒ์„ ๊ฐ€์ง€๊ณ  ์žˆ์œผ๋ฉฐ, ์ข‹์€ ์‚ฌ๋žŒ๋“ค๊ณผ ๋Š์ž„์—†๋Š” ๋…ผ์˜๋ฅผ ํ†ตํ•ด ์ธ์ƒ์˜ ๊ฐ€์น˜๋ฅผ ์ฐพ๊ณ ์ž ํ•˜๋Š” ์—ด๋ง์ด ์žˆ๋Š” ๋ถ„๋“ค๊ณผ ๊ฐ™์ด ํ•˜๊ณ  ์‹ถ์Šต๋‹ˆ๋‹ค. ์šฐ๋ฆฌ๊ฐ€ ๋‚˜๋ˆ„๋Š” ๋ชจ๋“  ๋Œ€ํ™”์™€ ํ–‰๋™์˜ ๊ฒฐ๊ณผ์—์„œ ํ•˜๋ฃจํ•˜๋ฃจ์˜ ์˜๋ฏธ๋ฅผ ์žฌ๋ฐœ๊ฒฌํ•˜์‹ค ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

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The key goals we expect are as follows:

As a Machine Learning Engineer, you will be a creative thinker and utilize data, machine learning, and software development skills to craft high-impact solutions that transform our business. The Machine Learning engineer will support our software developers, data analysts and data engineer on data initiatives and will ensure optimal data delivery throughout the project.

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Responsibilities include, but are not limited to:

  • Lead project to develop machine learning applications according to business requirements.
  • Write production ready code following modern programming design standards.
  • Analyze huge volumes of historical data to make predictions.
  • Collaborate to build out new API integrations to support continuing increases in data volume and complexity.
  • Perform data transformation and cleaning, feature building, model implementation.
  • Design end-to-end ML production systems, including project and data scoping, modeling strategies, and deployment requirements.
  • Work closely with backend and data engineers to build and deploy machine learning models.
  • Develop custom data algorithms.
  • Participate in code reviews to ensure code quality and share best practices and experiences with the team.
  • Design, implement and manage LLM projects.

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Required Technical and Professional Expertise:

  • Bachelor's Degree in Computer Science or related field.
  • 3+ years of experience as a Data Scientist, or Machine Learning.
  • Strong analytical skills related to working with unstructured datasets, including collecting, analyzing, interpreting, and presenting data.
  • Familiarity with data science concepts and machine learning techniques.
  • Advanced knowledge in SQL/Python.ย 
  • Comfortable working with Jupyter Notebook, TensorFlow, Pytorch, etc.
  • Strong problem solving skills.ย 



Not a must, but better if:

  • Experience with NLP tasks such as NER, text classification, etc.
  • Knowledge of vector embeddings, Retrieval Augmented Generation (RAG) and knowledge graphs.
  • Experience with relational SQL and NoSQL databases, including Postgres or HBASE, Apache Cassandra, etc.
  • Technical expertise with data models, data mining, and segmentation techniques.
  • Hands-on experience in SQL database design and data wrangling.ย 
  • Experience working with LLMs, prompt engineering, fine-tuning and model inference.
  • Desire to learn new skills and ability to adapt to new challenges.
  • Ability to speak and write in English (Business level required but fluency is not needed)

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Please check before you apply:ย 

  • Recruitment Type : full-time (3 months probationary period)
  • Working hours: Mon โ€“ Fri, 09:00 โ€“ 18:00
  • Location: 226 Bangbae-ro, Seocho-gu, Seoul
  • Documents to Submit: Resume (required), Portfolio (required)

* Application Format: Korean or English / Free Format / PDF
* Expired certificate is invalid and recruitment may be canceled if the contents of the application cannot be validated

* Salary range is an estimate based on our AI, ML, Data Science Salary Index ๐Ÿ’ฐ

Job stats:  12  4  0

Tags: APIs Cassandra Classification Computer Science Data Mining Engineering Hadoop HBase Jupyter Kafka LLMs Machine Learning ML models Model inference NLP NoSQL PostgreSQL Prompt engineering Python PyTorch RAG Spark SQL TensorFlow

Perks/benefits: Career development

Region: Asia/Pacific
Country: South Korea

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