Сколько на самом деле зарабатывают ML-инженеры

📌 Fill out the form now and receive a checklist of the "15 Most Popular Interview Questions and Answers" Pre-registration form: https://vk.cc/cO375f "ML Base" course: https://clck.ru/3Pq977 "ML in Business" course: https://clck.ru/3Pq9C4 Telegram MLinside: https://t.me/+xPCRRLylQh5lMmI6 Why do some ML engineers earn 150,000 rubles, while others earn 7 times more? In this video, Alexander Dubeykovsky (former Yandex, Avito, MLinside mentor) examines what really determines machine learning salaries in 2025—and why the salary range is so wide. In this video: • Real salaries for junior, middle, and senior ML specialists in Russia and Europe. • Which companies pay more – startups or big tech? • 6 factors that influence ML engineer income. • Why finance and product companies offer the highest salary ranges. • How ML (NLP, CV, tabular data) changes salaries. • When experience and soft skills are valued more than deep models. This video will be useful for those who: • are planning to enter data science and ML in 2025; • are already working in IT and thinking about switching to ML; • are preparing for an ML interview or salary discussion. Speaker: Alexander Dubeykovsky – ML expert, former Yandex and Avito employee, mentor and instructor at MLinside. Our "ML Basics" course helps you understand what influences salaries in the industry and prepare for your first job in machine learning. #machinelearning #datascience #MLsalaries #careers #MLinside If you enjoyed the video, please like and subscribe to the MLinside channel. Here you'll find honest analyses of salaries, interviews, and careers in data science. Timestamps: 00:00 — Introduction: Is ML really still the most expensive field in IT? 01:05 — Real salary ranges for Junior, Middle, and Senior specialists in Russia 02:05 — Data review from Levels.fyi: How salaries are divided by company 03:42 — Examples of ML engineer salaries at Yandex, Sber, and MTS 05:01 — Salaries of ML specialists in Europe and the US: comparison and taxes 07:47 — How much is left "net" after taxes and expenses abroad 08:11 — Course announcement and transition to factors influencing salaries 08:22 — Factor #1: Company size and business type 09:24 — Factor #2: Seasonality and market conditions 11:03 — Factor #3: ML industry and direction (finance, product, consulting, (research) 15:17 — Factors #4–6: Inflation, Specialization (NLP, CV), and Market Exceptions 17:10 — Results: Is ML a Myth or Reality of the 2025 Gold Mine?