aimlroles

ML Engineer (CSS)

Toss Open Β· verified Oct 22, 2026
Seoul Mid-level Machine Learning Engineering

At a glance

Mid-level Machine Learning Engineering role at Toss. Seoul.

Pay not stated ML Engineer salary

RoleMachine Learning Engineering
SeniorityMid-level
LocationSeoul
PostedAug 4, 2026 Β· 2mo ago
Role brief

aimlroles summary, based on the employer's posting.

What you'll do

  • Build credit and loan models, including demand, card-approval and credit-scoring predictions, to tailor services to users
  • Use data mining and machine learning methods to find new value and improve business outcomes
  • Explore varied datasets to identify opportunities for new services and business areas
  • Refine and optimize models as business priorities and growth plans change

What you bring

  • You have built and put alternative or credit-scoring models into use for individuals or sole proprietors
  • You can independently develop models and understand credit-scoring practices, rules and regulations
  • You have cleaned complex source data yourself and can work fluently with Python and SQL
  • You have applied machine learning or deep learning methods to solve real business problems

Who this fits

This role suits a mid-level ML engineer with hands-on credit-model development experience and the ability to take ownership of modeling. The position is based in Seoul, South Korea.

From the employer

πŸ“μ§λ¬΄ μΈν„°λ·°λ§Œ 봐도 100λ§Œμ›, ν•¨κ»˜ν•  νŒ€μ„ 직접 λ§Œλ‚˜λ³΄κ³  κ²°μ •ν•˜μ„Έμš”!
- 10μ›” 8일(λͺ©)λΆ€ν„° 25일(금)에 μ§€μ›ν•˜μ‹œλŠ” λΆ„λ“€μ—κ²Œλ§Œ ν•΄λ‹Ήλ˜λŠ” νŠΉλ³„ μ „ν˜•μ΄μ—μš”. 합격 여뢀와 상관없이 직무 인터뷰에 μ°Έμ—¬ν•˜μ‹œλ©΄ 100λ§Œμ›μ„ λ°›μ•„μš”. (μ œμ„Έκ³΅κ³ΌκΈˆ 본인 λΆ€λ‹΄)
- 총 7개의 직ꡰ, 40μ—¬κ°œμ˜ ν¬μ§€μ…˜μ—μ„œ μ±„μš©μ„ μ§„ν–‰ν•˜κ³  μžˆμ–΄μš”. (https://toss.im/career/event/ai-ml-data-2026)
- μ§€μ›ν•˜μ‹  ν¬μ§€μ…˜λ³΄λ‹€ 더 잘 λ§žλŠ” ν¬μ§€μ…˜μ΄ μžˆλ‹€λ©΄ 저희가 λ¨Όμ € μ œμ•ˆλ“œλ €μš”. λ‹€λ₯Έ 직무둜 λ³€κ²½ν•˜μ—¬ 인터뷰λ₯Ό μ§„ν–‰ν•˜μ…”λ„ 100λ§Œμ›μ€ λ™μΌν•˜κ²Œ μ§€κΈ‰λΌμš”.

# ν•©λ₯˜ν•˜κ²Œ 될 νŒ€μ— λŒ€ν•΄ μ•Œλ €λ“œλ €μš”
- ν† μŠ€μ˜ ML Engineer (CSS)λŠ” ν† μŠ€μ˜ Credit ModelingνŒ€μ— 속해 있고, λ‹€μ–‘ν•œ λ°±κ·ΈλΌμš΄λ“œλ₯Ό 가지고 μžˆλŠ” ML Engineer듀이 ν•œ νŒ€μœΌλ‘œ κ΅¬μ„±λ˜μ–΄ μžˆμ–΄μš”.
- Credit ModelingνŒ€μ€ λŒ€μΆœ, μΉ΄λ“œ, κ³„μ’Œ λ“± μ—¬λŸ¬κ°œμ˜ 금육 μ„œλΉ„μŠ€λ“€μ΄ λͺ¨μ—¬μžˆλŠ” Financial Marketplace 도메인에 μ†Œμ†λ˜μ–΄ λŒ€μΆœλΉ„κ΅ν”Œλž«νΌμ— μž…μ ν•œ κΈˆμœ΅μ‚¬λ“€μ„ μœ„ν•œ Risk Index λͺ¨ν˜•μ„ κ°œλ°œν•˜κ³ , 도메인내 λ‹€μ–‘ν•œ 금육 μ„œλΉ„μŠ€λ“€μ˜ λΉ„μ¦ˆλ‹ˆμŠ€ 문제λ₯Ό λ¨Έμ‹ λŸ¬λ‹μ„ 톡해 ν•΄κ²°ν•˜λŠ” 업무λ₯Ό λ‹΄λ‹Ήν•˜κ³  μžˆμ–΄μš”.
- **ν† μŠ€ 데이터 쑰직에 λŒ€ν•΄ 더 μ•Œμ•„λ³΄κ³  μ‹Άλ‹€λ©΄?** [β†’ *ν† μŠ€ Data Division μœ„ν‚€*](https://recruit-data-division.oopy.io/)

# ν•©λ₯˜ν•˜λ©΄ ν•¨κ»˜ ν•  μ—…λ¬΄μ˜ˆμš”
- λŒ€μΆœ λ‹ˆμ¦ˆ λͺ¨ν˜•, μΉ΄λ“œ λ°œκΈ‰ 예츑 λͺ¨ν˜•, μ‹ μš©ν‰κ°€ λͺ¨ν˜• λ“± μ‹ μš©κ³Ό λŒ€μΆœ 도메인 μ˜μ—­μ—μ„œ μœ μ €μ—κ²Œ λ§žμΆ€ν˜• μ„œλΉ„μŠ€λ₯Ό μ œκ³΅ν•˜κΈ° μœ„ν•œ λ‹€μ–‘ν•œ ML λͺ¨λΈμ„ κ°œλ°œν•΄μš”.
- Data Mining / Machine Learning / Deep Learning κΈ°μˆ λ“€μ„ ν™œμš©ν•˜μ—¬ μƒˆλ‘œμš΄ κ°€μΉ˜λ₯Ό λ°œκ΅΄ν•΄ λΉ„μ¦ˆλ‹ˆμŠ€ 고도화에 κΈ°μ—¬ν•΄μš”.
- μ‹ κ·œ μ„œλΉ„μŠ€ 및 사업 μ˜μ—­μ˜ λ°œκ΅΄μ„ μœ„ν•΄ λ‹€μ–‘ν•œ 데이터에 λŒ€ν•œ 뢄석 및 탐색을 μ§„ν–‰ν•΄μš”.
- λΉ λ₯΄κ²Œ λ³€ν™”ν•˜λŠ” λΉ„μ¦ˆλ‹ˆμŠ€ μ„±μž₯ μ „λž΅κ³Ό λ°©ν–₯성에 λ§žλ„λ‘ λͺ¨λΈμ„ κ³ λ„ν™”ν•˜κ³  μ΅œμ ν™”λ₯Ό μˆ˜ν–‰ν•΄μš”.

# 이런 λΆ„κ³Ό ν•¨κ»˜ν•˜κ³  μ‹Άμ–΄μš”
- 개인 ν˜Ήμ€ κ°œμΈμ‚¬μ—…μžμ˜ λŒ€μ•ˆ/μ‹ μš©ν‰κ°€λͺ¨λΈ(CSS)을 κ°œλ°œν•΄ λ‚΄μž¬ν™”ν•˜μ‹  κ²½ν—˜μ΄ ν•„μš”ν•΄μš”.
- μ‹ μš©ν‰κ°€λͺ¨ν˜• 개발 ν™˜κ²½, μ œλ„, κ·œμ œμ— λŒ€ν•œ 이해λ₯Ό λ°”νƒ•μœΌλ‘œ 독립적이고 주도적인 λͺ¨λΈλ§ κ²½ν—˜μ΄ ν•„μš”ν•΄μš”.
- λ³΅μž‘ν•œ μ›μ²œ λ°μ΄ν„°λ‘œλΆ€ν„° 데이터 μ •μ œ μž‘μ—…μ„ 직접 해보신 κ²½ν—˜μ΄ ν•„μš”ν•΄μš”.
- Python, SQL λ“±μ˜ μ–Έμ–΄λ₯Ό λŠ₯μˆ™ν•˜κ²Œ λ‹€λ£° 수 μžˆλŠ” 뢄이 ν•„μš”ν•΄μš”.
- Machine Learning / Deep Learning λ“± μ΅œμ‹  μ•Œκ³ λ¦¬μ¦˜μ„ ν™œμš©ν•œ μ‹€μ œ λΉ„μ¦ˆλ‹ˆμŠ€ 문제 ν•΄κ²° κ²½ν—˜μ΄ ν•„μš”ν•΄μš”.
- λ‹€μ–‘ν•œ μ΅œμ‹  κΈ°μˆ μ— 관심이 있고, μƒˆλ‘œμš΄ 것을 ν•™μŠ΅ν•˜κ³  μ„±μž₯ν•˜κΈ°λ₯Ό μ›ν•˜μ‹œλŠ” 뢄이 ν•„μš”ν•΄μš”.

# 이λ ₯μ„œλŠ” μ΄λ ‡κ²Œ μž‘μ„±ν•˜μ‹œλŠ” κ±Έ μΆ”μ²œν•΄μš”
- ν˜„μž¬ μ±„μš© 곡고와 κ΄€λ ¨ μžˆμ—ˆλ˜ 업무/ν”„λ‘œμ νŠΈ, κ·Έ 결과에 λŒ€ν•΄ ꡬ체적으둜 μž‘μ„±ν•΄μ£Όμ„Έμš”.
- ν”„λ‘œμ νŠΈμ—μ„œ μ–΄λ–€ λͺ¨λΈμ„ μ‚¬μš©ν–ˆλŠ”μ§€ ꡬ체적으둜 μž‘μ„±ν•΄μ£Όμ„Έμš”.
- Machine Learning λͺ¨ν˜•μ„ 직접 κ°œλ°œν•˜κ³  μ„œλΉ„μŠ€μ— μ μš©ν•΄ λ³Έ κ²½ν—˜μ΄ μžˆλ‹€λ©΄ ꡬ체적으둜 μž‘μ„±ν•΄μ£Όμ„Έμš”.
- 기술적으둜 μ™ΈλΆ€ κ³΅κ°œκ°€ λ―Όκ°ν•œ 사항일 경우, ν•΄λ‹Ή 뢀뢄은 μ œμ™Έν•΄μ£Όμ„Έμš”.

# ν† μŠ€λ‘œμ˜ ν•©λ₯˜ μ—¬μ •
- μ„œλ₯˜ μ ‘μˆ˜ > 직무 인터뷰 > 문화적합성 인터뷰 > 레퍼런슀 체크 > 처우 ν˜‘μ˜ > μ΅œμ’… 합격
- 직무 μΈν„°λ·°μ—μ„œλŠ” 심측 기술 λ©΄μ ‘κ³Ό ML λͺ¨λΈ 섀계λ₯Ό 주제둜 면접이 진행될 μ˜ˆμ •μ΄μ—μš”.

# ν•¨κ»˜ν•  λ™λ£Œλ₯Ό μœ„ν•œ ν•œλ§ˆλ””
> "금육 ν˜μ‹ μ˜ 주인곡은 λ‹Ήμ‹ μž…λ‹ˆλ‹€"
- Credit Modeling Team은 ν† μŠ€μ˜ 금육 λ„λ©”μΈμ—μ„œμ˜ 핡심 역할을 μˆ˜ν–‰ν•˜κ³  μžˆμ–΄μš”.
- λ‹¨μˆœνžˆ 주어진 문제λ₯Ό ν•΄κ²°ν•˜λŠ” 것이 μ•„λ‹ˆλΌ λͺ¨λΈλ§μ„ 톡해 λΉ„μ¦ˆλ‹ˆμŠ€μ˜ ν™•μž₯κ³Ό 고도화에 κΈ°μ—¬ν•  수 μžˆμ–΄μš”.
- 금육 κ΄€λ ¨ 데이터 뿐만 μ•„λ‹ˆλΌ λΉ„κΈˆμœ΅(λŒ€μ•ˆμ •λ³΄) 정보도 ν™œμš© κ°€λŠ₯ν•˜κ³  λ‹€μ–‘ λ¨Έμ‹ λŸ¬λ‹ 기법듀을 ν™œμš©ν•˜μ—¬ λΉ„μ¦ˆλ‹ˆμŠ€ μž„νŒ©νŠΈλ₯Ό κ·ΉλŒ€ν™” ν•  수 μžˆμ–΄μš”.