Senior MLOps Engineer (f/m/d)

Job description

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. 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 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. 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. Your Mission & Who We Are Looking For: MLOps at production scale. 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 ,…