adjoe builds the technologies behind mobile apps growth and monetization. With our core product Playtime Arcade, we've become the global leader in rewarded advertising, an ad unit built on a simple premise: users earn real in-app rewards for engaging with new apps. The result is one of the most effective value exchanges in adtech, connecting advertisers and publishers with over 770 million users annually. Architecting Intelligence to Optimize 200M+ Daily Decisions. As the intelligence core of our engineering organization, our Data Science team doesn't just deploy models, we engineer the fundamental decision engine that powers our platform
We aren't just optimizing clicks, we are dynamically calculating optimal reward structures to sustain a global value exchange. Engineered for performance, our stack leverages Tensorflow and PyTorch for model training, NVIDIA Triton to achieve sub-100ms inference. We own the full ML lifecycle from high-level research and feature engineering to deployment and A/B experimentation
Your Mission & Who We Are Looking For. MLOps at production scale
Задачи
Here, you will find the autonomy, the data depth, and the massive scale required to solve the most complex optimization challenges in the adtech ecosystem
Требования
You have 5+ years in MLOps or ML Engineering with a track record of deploying and maintaining models in high-traffic environments
At adjoe, that means keeping models fresh and performant across 2 billion+ daily requests, where decay in model quality directly impacts user experience and advertiser KPIs
Continuous Training & Automation
You design and manage CT pipelines and scheduling logic to ensure models stay current as new data flows in
You understand the end-to-end ML lifecycle well enough to know when a model needs retraining
Observability is part of the system, not an afterthought
You build monitoring systems that catch data skew, distribution shifts, and performance decay in production, using frameworks like Evidently, with alerts integrated directly into production pipelines
Low-latency serving under heavy load
You wrap deep learning models into production APIs and lead load testing to validate performance at scale
You're proficient in serving frameworks like Triton, ONNX Runtime, or TF Serving, and use deep-dive resource profiling to guide efficiency and optimization
ML platform ownership
You work with infrastructure teams to architect the ML platform, automated access to CPU/GPU clusters via Kubernetes, Docker, and orchestration tools like Airflow or Kubeflow, so data scientists can focus on models, not infrastructure
Plus: AdTech industry background. You understand how ad delivery systems work and the business logic underneath
Условия
At adjoe, you’re not here to just close JIRA tickets, you’re helping build the infrastructure behind one of the most impactful platforms in adtech
The systems you work on will reach hundreds of millions of users and power billions of decisions every day
Go Big. Own projects with impact on 770M users and push adtech boundaries
Move Fast. Ship solutions multiple times a day, learn from results, and keep momentum
Be Direct. Solve problems openly and collaborate across teams
Thrive Together. Grow with a diverse, global team of people from over 40 different countries that learn from each other
Have Fun. Celebrate wins, enjoy daily victories, and bring your energy
We welcome applications from people who will contribute to the diversity of our company
At a scale of 770 million users and 100,000+ predictions per second, we are solving a multi-objective optimization problem that balances user incentives, advertiser ROI, and long-term platform health in real time
Our architecture is built on a 1PB+ behavioral data lake, providing the high-fidelity input necessary to train deep learning models that predict individual user engagement with precision
We welcome applications from talent worldwide and provide relocation support to Hamburg, Germany for those ready to join our team
Паспорт вакансии
История публикации
Появилась в Вакандии26 дней
Перепубликациинетпубликовалась один раз
Проверяли на источникеВидели сегодня
Среди похожихНет данныху карточки не хватает полей, чтобы найти похожие
Откуда что взялось
Отмечено то, что вывели мы. Без пометки — значение назвал работодатель.
ГрейдSeniorвычитано из текста вакансии
Формат работыне указан
ГеографияГамбург, Германиявычитано из текста вакансии
Зарплата≈ 17 354 USD в месяцнаша оценка, в вакансии не названа
Почему на этом месте в выдаче
Порядок выдачи объявлен контрактом: свежесть решает между днями, полнота и зарплата — внутри дня.
Полнота карточки753 из 4 полей: грейд, формат, география, зарплата
Зарплата названа0вилки работодателя нет, показана наша оценка
Проверка Вакандии
Источники и свежесть
Тип источника
Карьерный сайт работодателя
Найдено публикаций
1
Посмотреть публикации и даты
ashbyОсновная публикация · 2026-05-22
A
Работодатель
adjoe
37 активных вакансий · вилка работодателя указана в 0%