Deep Learning Engineer at SQUAD (Kyiv, Ukraine)

Job Summary

As a member of our Research Team, you will be engaged in an iterated process of data preparation, prototyping and/or implementation, measurement, and deploying your solution together with the back-end team. You will work closely with members of our Research and Engineering teams to improve models and bring products to the market based on your research.

Qualifications and Skills

  • 2+ years in machine learning (computer vision domain)
  • Practical experience in at least one of the following problems: classification, detection, segmentation.
  • Python3, numpy, scikit-learn, pandas, scipy.
  • Deep learning frameworks:TensorFlow or PyTorch is a must; Keras is plus.
  • Experience in deploying machine learning models to production
  • Good understanding of machine learning and deep learning concepts.
  • Good written and spoken English

Nice to have

  • Practical experience with GANs, VAEs
  • Probabilistic programming and bayesian framework
  • Model optimization: pruning, quantization, knowledge distillation
  • Basic understanding of web and client-server architecture
  • asyncio, aiohttp, and other async libraries for back-end
  • Basic understanding of Big Data, understanding of difference between MapReduce and in-memory processing
  • Algorithms, data structures
  • SQL, NoSQL
  • Docker, Kubernetes, Kubeflow

We offer multiple benefits that include

  • Working on impactful security products and the opportunity to use them personally
  • Competitive salary and perks
  • PE accounting and support
  • WFH and remote working mode possibility. Partial furniture compensation
  • Social package, including medical insurance available from the start date and sports compensation after the trial period
  • 18 paid vacation days per year, paid public holidays according to the Ukrainian legislation
  • Educational possibilities like corporate courses, knowledge hubs, and free English classes. Semiannual performance review
  • Free meals, fruits, and snacks when working in the office.

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