Experience with machine translation, multilingual NLP, efficient long-context modeling, language quality estimation, or multimodal machine translation
Experience designing evaluation and reward signals using automatic metrics, Model-as-judge evaluation, non-verifiable rewards, and human-in-the-loop evaluation
Experience with multi-objective optimization, consistency models, unified multimodal generation
Experience with diffusion models
Publications at top-tier venues
You are welcome at DeepL for who you are - we appreciate authenticity here. Our product is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all succeed, contribute, and think forward! So bring us your personal experience, your perspectives, and your background. It’s in our diversity that we will find the power to break down language barriers in the world
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