Competera

ML Engineer

$$$$
Product

Competera uses Big Data and Deep Learning to change the way retailers do pricing. We are known for both: cutting-edge math ’under the hood’ and for deep expertise in the pricing domain. We are looking for an MLOps Engineer to develop the platform that supports the entire ML models lifecycle in production.

You could be a perfect match for the position if
You want to:
 

  • Develop tools for Data Scientists that help them to scale in their operations and routines.
  • Develop pricing algorithm/systems based on DS research; convert prototypes to robust systems for enterprise retailers.
  • Build/optimize ML and mathematical algorithms. 
  • Improve Data Scientists’ Dev Experience.
  • Create/optimize data pipelines and systems for ML models.
  • Optimize SDLC for ML models.
  • Implement backend systems to expose ML systems for other platform components.

 

You have:

  • Education in STEM-related fields, Computer Science/Math/Physics is preferable.
  • Understanding of ML models training lifecycle.
  • Python experience 5+ years.
  • Ability to understand key ML principles and terms (loss, training loop, gradient descent, statistics, etc.).

 

You’re gonna love it, and here’s why:
 

  • Rich innovative software stack, freedom to choose the best suitable technologies;
  • Remote-first ideology: freedom to operate from the home office or any suitable coworking;
  • Flexible working hours (we start from 8 to 11 am) and no time tracking systems on;
  • Regular performance and compensation reviews;
  • Recurrent 1-1s and measurable OKRs;
  • In-depth onboarding with a clear success track;
  • Competera covers 70% of your training/course fee;
  • 20 vacation days, 15 days off, and up to one week of paid Christmas holidays;
  • 20 business days of sick leave;
  • Partial medical insurance coverage.

Drive innovations with us. Be a Competerian.

Required languages

English B2 - Upper Intermediate
Ukrainian Native
Published 26 June
13 views
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3 applications
Last responded more than a month ago
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