{"id":3245887,"url":"https://alion.io/job/avroradigital-ml-inzhener-cv","title":"ML инженер CV","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":"CNN","optional":false},{"name":"Computer Vision","optional":false},{"name":"Docker","optional":false},{"name":"Git","optional":false},{"name":"Linux","optional":false},{"name":"ONNX","optional":false},{"name":"OpenCV","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"ResNet","optional":false},{"name":"RT-DETR","optional":false},{"name":"TensorRT","optional":false},{"name":"YOLO","optional":false},{"name":"C++","optional":true},{"name":"GStreamer","optional":true},{"name":"OCR","optional":true},{"name":"OpenVINO","optional":true}],"status":"live","first_seen_at":"2026-10-25T19:01:01Z","employer_posted_date":"2026-10-25","last_verified_at":"2026-11-01T19:41:34Z","board_verified":true,"closed_at":null,"days_open":7,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":7},"description":"Computer Vision инженер\n Мы развиваем продукты на базе компьютерного зрения: анализ видеопотоков с камер, детекция и трекинг объектов, распознавание на изображениях. Модели уже работают в продакшене, и мы ищем инженера, который будет отвечать за их качество и скорость - от датасета до инференса на железе.\n Если ты умеешь не только обучить модель в ноутбуке, но и довести её до стабильной работы на реальном видеопотоке - ждём тебя.\n Обязанности\nОбучать и дообучать модели детекции, сегментации и классификации изображений (YOLO, RT-DETR, U-Net и аналоги);\nСтроить пайплайны обработки видеопотоков: детекция, трекинг, подсчёт и аналитика событий;\nГотовить и развивать датасеты: постановка задач разметки, контроль качества, аугментации;\nОптимизировать модели для инференса: ONNX, TensorRT, квантизация, работа на GPU и edge-устройствах;\nВыводить решения в production и поддерживать их работу;\nОценивать качество: mAP, precision/recall, разбор ошибок на реальных данных;\nСледить за SOTA-решениями, экспериментировать и внедрять то, что работает.\n Требования\nУверенный Python и PyTorch;\nОпыт обучения моделей детекции и/или сегментации от постановки задачи до продакшена;\nЗнание OpenCV и классических методов обработки изображений;\nПонимание архитектур CNN и трансформеров для зрения (ResNet, ViT, DETR);\nОпыт оптимизации инференса (ONNX, TensorRT или аналоги);\nDocker, Linux;\nGit, культура код-ревью;\nОпыт от 3 лет.\n Будет плюсом\nОпыт работы с видеопотоками: RTSP, GStreamer, DeepStream;\nОпыт с edge-устройствами: NVIDIA Jetson, OpenVINO;\nC++ для критичных по скорости участков;\nОпыт с OCR, 3D/SLAM или генеративными моделями;\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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