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

Adjoe, Berlin

What you will do

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.

Adjoe, Berlin

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.