{"id":3246109,"url":"https://alion.io/job/avrora-digital-ml-inzhener-recsys","title":"ML-инженер RecSys","company":{"id":4424741,"name":"Avrora Digital","domain":"avrora-digital.ru","url":"https://alion.io/company/avroradigital","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"HeadHunter","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Moscow, Russia"],"countries":["RU"],"hiring_countries":["RU","BY","KZ","KG","AM"],"hiring_countries_total":5,"salary":null,"salary_estimate":{"min_usd":26000,"max_usd":55000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":11},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CatBoost","optional":false},{"name":"Git","optional":false},{"name":"Hadoop","optional":false},{"name":"LightGBM","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"XGBoost","optional":false},{"name":"FAISS","optional":true},{"name":"LLM","optional":true}],"status":"live","first_seen_at":"2026-10-25T19:02:33Z","employer_posted_date":"2026-10-25","last_verified_at":"2026-10-29T14:28:50Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"ML-инженер (рекомендательные системы)\n Мы развиваем рекомендации и персонализацию в продукте с миллионами пользователей: ленты, подборки, поиск и ранжирование выдачи. Рекомендательная система уже работает в проде, и мы ищем инженера, который будет растить её качество и продуктовые метрики.\n Если ты умеешь не только обучить модель ранжирования, но и доказать её эффект в A/B-тесте - ждём тебя.\n Обязанности\nРазвивать многостадийную рекомендательную систему: кандидатогенерация, ранжирование, реранкинг;\nОбучать и улучшать модели: коллаборативная фильтрация (ALS, implicit), two-tower, бустинги для ранжирования (CatBoost, LightGBM), sequential-модели;\nРешать проблемы холодного старта, разнообразия и популярности в выдаче;\nСтроить фичи и пайплайны данных на больших объёмах (Spark, Airflow);\nВыводить модели в production и следить за их качеством;\nОценивать качество: офлайн-метрики (NDCG, MAP, Recall@K, coverage) и онлайн A/B-тесты;\nГенерировать продуктовые ML-гипотезы и проверять их вместе с аналитиками.\n Требования\nУверенный Python и SQL;\nОпыт разработки рекомендательных систем или поискового ранжирования в продакшене;\nЗнание подходов: коллаборативная фильтрация, learning to rank (pointwise, pairwise, listwise), эмбеддинги;\nОпыт с бустингами (CatBoost, LightGBM, XGBoost) и PyTorch;\nОпыт работы с большими данными: Spark / PySpark, Hadoop;\nОпыт проведения и анализа A/B-тестов;\nGit, культура код-ревью;\nОпыт от 3 лет.\n Будет плюсом\nОпыт с sequential-рекомендациями: SASRec, BERT4Rec;\nОпыт ANN-поиска: FAISS, HNSW;\nОпыт с CTR-моделями и рекламным ранжированием;\nПрименение LLM в рекомендациях и поиске;\nАнглийский для чтения документации и статей;\nПрофильное техническое образование.\n Условия\nУдалённо или гибрид;\nДМС;\nБонусы и премии по результатам;\nОформление по ТК РФ;\nКомпенсация спорта;\nОбучение за счёт компании, участие в 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