Senior ML Research Scientist – AI & Generative Recommendation (m/f/d)

What’s in It for You?

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.

PhD or equivalent research depth, with a production track record. You have a PhD in CS, Statistics, Math, or Physics, or an MSc with extensive industry research, and publications in top-tier venues like SIGIR, KDD, NeurIPS, or ICLR. You've spent 4+ years building large-scale recommender systems in high-throughput environments: AdTech, e-commerce, or social.

You translate research into production, not just prototypes. You distill SOTA research into robust, production-ready code optimized for sub-millisecond latency. At adjoe, that means architecting multi-stage recommendation pipelines, Generative Retrieval, Deep Ranking, and Re-ranking, serving millions of requests across a stack built on 770 million users' behavioral data.

Deep expertise in PyTorch and large-scale model optimization. You have hands-on experience with high-concurrency optimization, model quantization, and distillation, the engineering side of getting large generative models to run under extreme production constraints, not just in research environments.

Content understanding and embeddings are core to your work. You research and develop information-rich user and ad embeddings using unsupervised and generative approaches, directly improving the quality of predictions across both Playtime and adjoe Ads.

Raises the technical bar around them. You lead architectural debates, mentor junior scientists, and drive the team's research direction without needing a roadmap handed to you.

What’s the Team & Tech Stack?

The Data Science Research team builds foundational ML representations and advanced AI capabilities that enhance both Playtime and adjoe Ads. It leverages generative approaches to deliver systems such as high-dimensional user embeddings, general content understanding, and sequential recommendation. The stack focuses on deep learning and heavy data throughput: PyTorch and [Ray/Hugging Face] for training/fine-tuning foundation models, Trino and Athena for querying massive user-behavior data lakes, and dedicated GPU infrastructure.

Location & Benefits

We welcome applications from talent worldwide and provide relocation support to Hamburg, Germany for those ready to joi