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

We are looking for a Machine Learning Engineer for its ML team that is primarily working on various supervised and reinforced ML problems. The team is developing models for text classification, clustering and regression tasks. Working knowledge of Bayesian statistics and reinforcement learning algor

As a ML engineer you will have a unique opportunity to not only work on existing datasets that span for years of historical customer data, gather and analyze new features best suitable for your tasks and train state of the art ML models, but also help build new processes to collect the required data points and collaborate with other teams to integrate your ML models into Web, mobile or database applications.

Requirements:
— 3+ years experience developing ML models
— 2+ years of full-stack development preferably in a startup engineering environment using various programming languages
— Deep understanding of ML algorithms and concepts. Ability to efficiently use deep learning. Record of solving problems with these methods
— Proficiency in solving of NLP problems
— Strong proficiency in querying and manipulating large datasets for analytical purposes using SQL
— Strong analytical and problem solving skills
— Good communication and relationship skills
— Strong proficiency in Python for ML
— Proficiency with data visualization tools
— Bachelor’s Degree — preferably a Master’s or PhD in Computer Science, Engineering, Statistics, or Information Systems

We offer:
— Time off throughout the year (paid and unpaid)
— Professional development support and encouragement
— Resources to help improve your overall well-being
— Free membership to dive into JA product

Responsibilities:
— Gathering and analyzing data sources; developing data insights and preparing data for use in ML algorithms
— Building scalable and efficient models on large datasets to solve various classification, clustering or regression tasks
— Using state of the art ML techniques and applying transfer learning
— Integrating ML models into existing or new applications using the CI/CD approach
— Maintenance of ML models in production environment (monitoring, performance optimizations, tracking etc.)