Position: Machine Learning Engineer
Company: inVerita
Category: Data Science
Experience required: 5y
English level: upper

We are looking for an experienced ML Engineer to join our team of professionals! You will craft custom ML-based solutions, focused on NLP and text processing, then fit them on TBs of data. You will work with cutting-edge architectures and techniques, then deploy your models in a massively parallel Big Data environment. You will be required to make domain-specific, data-oriented decisions, based on a vast and complete world-view.

**Project:**
• Platform provides business data enrichment with unprecedented coverage of private companies, accurate classification, and in-depth insights based on real-time updates, for procurement, insurance, market intelligence, and more.

**Requirements:**
• 5+ years of prooven working experience as Machine Learning Engineer;
• Previous experience improving datasets through heuristics, training and optimally deploying your models;
• Proficiency with Python, as well as MySQL or any relational database;
• Background in ML frameworks, libraries, data structures, data modeling, and software architecture;
• Knowledge of advanced math and statistical concepts and techniques;
• High speed and uncompromising quality in your work;
• A growth mindset, able to capitalize on unprecedented contexts through your skills and abilities;
• An appetite to grapple with a variety of technical challenges;
• The ability to quickly and effectively evaluate technical tradeoffs and translate them into relevant scenarios;
• Consistent performance over time coordinated with a matching set of values;
• Previous experience with an NLP framework (Transformers, SpaCy) and ML framework (PyTorch, Sklearn, Tensorflow) will be a plus; 
• English level: Upper Intermediate.

**Responsibilities: **
• Develop new, novel algorithms and techniques for extracting and analyzing (very!) large and complex sets of data;
• Gather and build appropriate training datasets to create and improve models;
• Train and evaluate your model in a massively parallel environment;
• Own your ML models end-to-end, from collecting training data to deployment and monitoring in production;
• Optimize your ML solutions for the production environment;
• Improve NER extraction and text classification algorithms;
• Perform statistical analysis of the real-world results of your models;
• Prototype quickly to solve thorny use cases, without getting stuck in theory, as we're prone to shipping early and often;
• Write well-designed, testable, efficient code;
• Identify areas of opportunity and improvement.