Вакансия Machine Learning Team Lead

21 июля 2021    34

Apalon is a leading mobile development company. The unique blend of technology and art that make up the company’s DNA, enables Apalon to deliver unrivaled products. Apalon's portfolio includes award-winning titles such as NOAA Radar, Weather Live, Productive, Sleepzy and more. Apps by Apalon have earned massive recognition with 600+ MLN downloads, received numerous prestigious awards, and have been featured by Apple, Google, Amazon, Samsung, as well as world's leading press.

We are looking for an AI/ML Team Lead.


  • Provide technical leadership for a team of ML engineers, creating and supporting a culture of knowledge sharing, technical excellence and fun.
  • Work closely with product teams to integrate ML in product strategy, build ML enabled features in new and existing products.
  • Be responsible for team growth, hire, retain, develop, mentor and coach team members to unlock their potential.
  • Dive deep in current processes and be able to advise improvements if necessary.
  • Propose, create and improve development infrastructure for ML projects.


  • 2+ years leading a team of engineers, working collaboratively with product and engineering teams.
  • 4+ years of experience building top notch technical solutions including high performance ML products.
  • Broad understanding of ML/CV approaches and practices. Deep Learning: classification, detection, segmentation (semantic + instance).
  • Experience with deep learning frameworks (TuriCreate/CoreML, Tensorflow, Pytorch, etc.).
  • Production experience with building ML pipelines including CI/CD, ML Ops.
  • Strong Python programming skills.
  • Knowledge of basic OOP, computer science algorithms and data structures.
  • Communicate openly. Ability to build a collaborative environment.


  • BS/MS/PhD degree in Computer Science, Math or Physics.
  • Experience with C++ or mobile development.
  • Experience with ML on mobile platforms (iOS & Android).
  • Solid experience with Linux, Git.
  • Working knowledge of cloud computing (GCP or AWS) and tools and technologies Kubernetes, Docker, etc.

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