Position: Machine learning engineer (Senior)
Company: Govital
Experience required: 2y
Category: Python
English level: upper
Published: 2020-05-01T00:00:00+03:00

Required skills
— Strong knowledge in machine learning fundamentals i.e. linear models, decision trees, naïve bayes, clustering algorithms, dimensionality reduction (PCA, t-SNE) and a good grasp of the strengths and weaknesses of specific approaches.
— Good foundation in basic statistics and linear algebra;
— Strong Python knowledge;
— Strong practical experience with Deep Learning frameworks like PyTorch, Tensorflow or Keras;
— At least minor experience with python visualization tools(matplotlib/seaborn, Plotly);
— Comprehensive knowledge of the Python data analyses ecosystem (Pandas, Numpy, Scikit-learn etc.);
— At least minor experience with natural language processing;
— Experience working in Linux, Unix environment.

As a plus
— Experience with Tranformers architecture (GPT-2, BERT, etc);
— Understanding SOTA approaches for machine learning problems in NLP domain;
— Experience of work with LSTM models.

We offer
Working environment:
Given the ongoing quarantine, we’ve established a remote workflow that promotes quality performance and comfortable communication.
Our employees have access to all the necessary equipment and services required for effective work from home.

Today you can enjoy the following perks:
— Flexible working hours
— Corporate insurance (after the probation period.)
— 18 working days of paid vacation. 20 paid sick leaves a year
— Employment as a private entrepreneur (all taxes paid by the company.)
— Complete support from managers and colleagues
Once quarantine is over, we’ll be glad to offer you a full compensation package and more benefits from our company.

Responsibilities
— Participate in a massive NLP project from the early prototype stage;
— Research and implement approaches to improve project result quality and optimize speed;
— Move from prototype to production, ensuring scalability of models training and models inferring.