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. Data engineering at ML 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 data engineering on a modern stack, with proven experience handling systems that process several TB of data per day and thousands of events per second
Flink is your default for real-time
You have hands-on experience with Apache Flink for stateful stream processing and real-time feature computation
You'll translate Data Science requirements into high-performance streams, enable Data Scientists to contribute new features and implement features yourself when needed
Pipeline bottlenecks don't survive long around you
You know how to identify and eliminate inefficiencies in complex ETL jobs, whether handling high-volume batch processing or real-time streaming systems like Kafka
At adjoe, data velocity is critical: slow pipelines directly impact model freshness and prediction quality at sub-100ms inference
You think in layers, not tables
You move beyond raw data to design robust, multi-layered data architectures using dbt, and can reason confidently about storage formats, Data Lakehouse models, and what belongs where in a 1PB+ data lake
Familiarity with Medallion Architecture is a strong plus
The bridge between Data Science and Backend. You've worked closely with Data Scientists on online ML systems with low latency requirements, and understand what it takes to make experimental research production-ready. Strong Java required
Go or Python a plus
Owns quality, not just delivery
You implement data validation, monitoring, and governance processes in production, because in a system where model inputs directly drive advertiser ROI, data integrity isn't optional
Experience with Airflow, Kubeflow, AWS, Terraform, and Kubernetes is a strong plus
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
The data infrastructure behind this runs at real scale: 2TB ingested in real time every day, 100+ Airflow jobs and data pipelines, and ML models handling p99 latency of 100ms across 100,000+ predictions per second
As a Data Engineer on this team – you'll be building and maintaining the infrastructure that makes them possible: the Feature Store, the data pipelines, the data quality systems, and the data architecture that data scientists depend on to move fast
We welcome applications from talent worldwide and provide relocation support to Hamburg, Germany for those ready to join our team
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ashbyОсновная публикация · 2026-05-26
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