ML разработчик

Mad Devs is a full-stack team specializing in developing and administering large-scale IT products across diverse industries. Its products operate in Europe, the USA, and Southeast Asia, including AI solutions for the legal industry.

• Develop and implement machine learning algorithms;
• Transfer machine learning models from development to real-world application;
• Compare different approaches to solve complex business problems;
• Collaborate with customers to identify their problems and opportunities for solving them using machine learning tools;
• Develop algorithms for recommendation and ranking systems;
• Develop intelligent assistants;
• Keep models and instructions for language models up to date.

• At least 1.5 years of experience in startups or professional services firms;
• Familiarity with software development and delivery processes;
• Knowledge and experience in developing and tuning machine learning models for classification, regression, clustering, and other tasks;
• Experience integrating off-the-shelf solutions;
• Experience working with big data and systems for processing it;
• Ability to use data analysis to identify insights and patterns for solving business problems;
• Mathematical background;
• Knowledge of basic machine learning algorithms and metrics;
• Experience implementing machine learning models in a production environment;
• Experience with natural language processing (NLP);
• Experience with large language models (LLM);
• Experience developing agent systems with frameworks such as LangChain and LangGraph, including multi-tool and multi-agent systems;
• Customer negotiation skills for identifying needs and objectives;
• Ability to gather and analyze requirements and translate business requirements into technical specifications;
• Ability to define clear and achievable goals for machine learning tasks and projects;
• Ability to conduct regular customer demos and explain technical aspects and achievements accessibly;
• B2 English and fluent Russian.

• Flexible working hours;
• Remote-first culture;
• Long-term projects;
• Salary in dollars;
• Professional communities;
• Onsite business trips;
• Training budget;
• Paid conferences.