Senior Research Scientist | Model Scaling

DeepL is a global AI product and research company focused on building secure, intelligent solutions to complex business problems. We are looking for a Senior Research Scientist to own the foundational modelling decisions behind the next generation of our most capable translation models. This is a high-impact, hands-on role for a researcher who can prototype rapidly, run large-scale experiments, and drive modelling choices all the way into production.

Responsibilities

Own the foundational modelling decisions behind the next generation of our most capable translation models; Prototype rapidly, run large-scale experiments, and drive modelling choices all the way into production; Decide which foundation models we build on, which architectures we adopt as we scale, and how we adapt a strong base model into a highly capable translation system; Drive the selection and evaluation of open foundation / open-weight models as the basis for our next-generation translation systems; Lead model selection and general architecture decisions for scaling to hundreds of billions of parameters, including Mixture-of-Experts and other sparse or efficient designs; Design multi-capability adaptation strategies using LoRA, PEFT, and related methods; Own the modelling lifecycle for your work: prototyping, ablations, scaling experiments, evaluation, and delivery into production, with rigorous and reproducible evaluation; Partner closely with post-training, RL/RLHF, and instruction-following specialists to integrate alignment and capability work into the base model; Stay ahead of the open-model and scaling literature, and bring well-founded recommendations back to the team

Requirements

Strong hands-on experience adapting and scaling large language models via fine-tuning, instruction-tuning, or post-training of multi-billion-parameter models beyond black-box use; Sound judgment about architecture trade-offs at scale (e.g. dense vs. MoE) and about which open-weight foundation models to build on; Working knowledge of parameter-efficient and multi-capability adaptation (LoRA/PEFT and variants); A hands-on builder who enjoys training models, running experiments, and debugging pipelines, and who can carry research results through to production with engineering; Strong coding and experimentation skills (Python, PyTorch/JAX/Tensorflow); Ability to communicate clearly, collaborate across teams, and align research work with product and engineering priorities

Nice to have

Experience quantifying uncertainty in large models — calibration and confidence estimation via Bayesian methods, ensembling, steering, or prompt-based approaches; Experience with machine translation, multilingual NLP, or document-/layout-aware modelling experience; Familiarity with MoE-specific training and adaptation (e.g. expert routing, Mixture-of-LoRA-Experts) and large-scale data-mixture design

Benefits

Diverse and internationally distributed team; Open communication, regular feedback; Hybrid work, flexible hours; Virtual Shares; Regular in-person team events; Monthly full-day hacking sessions (Hack Fridays); 30 days of annual leave; Competitive benefits