deepl - Senior Research Scientist | Multimodal Systems

• We are looking for a Senior Research Scientist to lead fine-tuning, post-training, and reinforcement learning for the next generation of DeepL's document translation multimodal and vision models. This is a high-impact, hands-on role for a researcher who can own a major research direction, prototype rapidly, run large-scale experiments, and drive breakthroughs all the way into production. • You will develop models that reason about document layout by fusing expert, real world and synthetic data, while leading efforts to make our translation highly steerable and adaptable. • Drive the development of vision and multimodal models for document, image and media translation, ranging from media ingestion and generation to end-to-end models. • Drive hands-on research and development on post-training for our vision and/or multimodal models: supervised fine-tuning, knowledge distillation, preference optimization, and reinforcement learning tuned to translation quality. • Build evaluator models for document and design quality, including rubric- and reference-based grading, and investigate and mitigate reward hacking and quality-estimation failure modes. • Own the full lifecycle of model delivery: prototyping, ablations, training, evaluation, optimization, and production deployment, working closely with engineering to ship into real-time systems at scale. • Establish strong practices for evaluation, reproducibility, monitoring, and continuous model improvement in production. • QUALITIES WE LOOK FOR • Proven experience with developing multimodal models, VLM, and/or vision models. • Deep, hands-on expertise in model post-training, knowledge distillation (teacher-student training), and/or reinforcement learning (RLHF/RLAIF, PPO/GSPO, and reward modeling). • Strong data-centric instincts for building synthetic-data and preference-data pipelines, Model-as-judge generation, data curation and filtering, data augmentations, and/or reasoning about data mixtures and ablations. • A hands-on builder who enjoys training models, running experiments, debugging pipelines, and integrating ML systems into production while staying grounded in product impact and real-world quality. • Strong coding and experimentation skills (Python, PyTorch/JAX/Tensorflow), and the ability to communicate clearly and align research with product and engineering priorities. • Ability to lead complex research efforts, to communicate clearly and collaborate across teams, while staying grounded in product impact, user experience, and real-world performance.