Machine Learning Engineer

Published on Sep 07, 2021

A Product company with office in Kyiv, Ukraine.

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1-3 years
Job Type:
Data Engineering
Deep Learning
Data Science & AI

What You Will Do Responsibilities

• Research new algorithms to solve Competera client's problems. Apply it in quick prototypes
• Deliver Deep NN models to production
• Deploy, version and scale ML algorithms selected as ‘core’ to serve Competera customers
• Introduce MLOps practices

What You Should Bring

• MSc/BSc in Computer Science or similar degree. Knowledge of statistics, probability theory and linear algebra.
• Good knowledge of ML theory and practice — pros & cons of different model types, validation, metrics, hyperparameter tuning, interpretability
• Experience with Tensorflow and Keras (can implement custom NN model from paper)
• Practical experience with ML models in production (2+ years)
• Experience with distributed model training and distributed feature engineering (Spark/Dask/Apache Beam)
• Python 3.х, software engineering skills (able to produce well-structured production-level projects, not only notebook scripts)


• Experience with distributed training for structured data (tabular, time-series), and time series forecasting
• Understanding MLOps: pipelines, data validation, automated model training, data & model versioning, model monitoring
• Experience with Docker
• Practical experience with machine learning pipelines to orchestrate complicated workflows (Kubeflow/Airflow etc)

What You Will Get Benefits

• Meaningful work in an agile team of engineers, who turn business ideas into software solutions
• Fair payout with regular performance-based reviews and stock options plan for top performers
• Remote-first ideology: freedom to choose between a pet-friendly coworking and a home office even after the pandemic
• Working hours that adapt to your biorhythm, and no time tracking on
• Want to learn? Competera loves that and is eager to cover 60% of your training/courses fee
• Paid vacation & sick leaves (20 business days each) + 15 days off
• Partial medical insurance coverage

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