Senior Research Scientist | Model Scaling

MEET DEEPL DeepL is a global AI product and research company focused on building secure, intelligent solutions to complex business problems. Over 200,000 business customers and millions of individuals across 228 global markets today trust DeepL's Language AI platform for human-like translation, improved writing and real-time voice translation. Founded in 2017 by CEO Jaroslaw “Jarek” Kutylowski, DeepL now has around 1,000 passionate employees and is supported by world-renowned investors including Benchmark, IVP, and Index Ventures. Our goal is to become the global leader in trusted, intelligent AI technology, building products that drive better communication, foster connections, and create a meaningful impact. To achieve this, we need talented people like you to join our journey. If you’re ready to shape the future of AI and grow your career in a fast-moving, purpose-driven environment, DeepL is your next destination. WHAT SETS US APART What sets us apart is our blend of cutting-edge AI technology, meaningful work, and a culture where people truly thrive. We’re a team of innovators, researchers, and creators driven by a shared purpose to unlock human potential by making work simpler, smarter, and more connected. When we share what it’s like to work at DeepL, the reactions are overwhelmingly positive. This might be because of our technology that helps millions of people and businesses communicate and work better every day, or because of the trust, curiosity, and care that shape our culture. What we know for sure is this: being part of DeepL means joining a team dedicated to innovation, growth, and well-being. Discover more about life at DeepL onLinkedIn https://www.linkedin.com/company/deepl/,Instagram https://www.instagram.com/deepl_official/, and our Blog https://www.deepl.com/en/blog. MEET THE TEAM BEHIND THIS JOURNEY Our Language AI teams form the foundation of DeepL's success. We are a dedicated group of researchers who collaborate closely with engineers, product managers, and designers. Our mission is to build the world's leading language AI system to deliver perfect translations for the most demanding use cases. To that end, we take responsibility for the entire life cycle of the machine learning models that power our language AI products. This includes data, training, quality assurance, and operational aspects. In our highly collaborative teams, each person has the scope to drive impact across the company. YOUR RESPONSIBILITIES 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. In this competitive and highly dynamic field, you will 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 working side by side with our post-training, RL, and instruction-following experts. YOU WILL: - 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. QUALITIES WE LOOK FOR - 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 ap