deepl - Senior Research Scientist | Model Steering

• We are looking for a Senior Research Scientist to lead fine-tuning, post-training, model-steerability, and reinforcement learning for the next generation of DeepL's LLM-based translation 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. • Your central focus is how we blend data and shape model behaviour after pre-training. You will fuse high-value human expert data with synthetic data, and you will lead the effort to make our translation models steerable, following custom user instructions, rules, and context. You will work with model architectures at the scale of hundreds of billions of parameters. • Drive the development of translation models that are steerable conditioned on user preferences, rules and context. • Drive hands-on research and development on post-training for our core translation models: supervised fine-tuning, knowledge distillation, preference optimization, and reinforcement learning tuned to translation quality. • Build reward models and evaluator models for translation, including rubric- and reference-based grading, and investigate and mitigate reward hacking and quality-estimation failure modes. • Drive an agenda toward models that ingest multimodal content and context to increase translation quality. • 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. • Mentor researchers and engineers, promote hands-on collaboration, and raise the bar for model quality. • QUALITIES WE LOOK FOR • Proven experience making large models steerable and instruction-following by identifying the most effective method to instill a given behavior, drawing from instruction tuning, latent space methods, steering vectors, and/or constrained encoding and decoding methods. • Deep, hands-on expertise in LLM post-training (SFT, DPO), 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, LLM-as-judge generation, data curation and filtering, and reasoning about data mixtures and ablations. • Experience designing evaluation and reward signals using automatic metrics, LLM-as-judge evaluation, non-verifiable rewards, and human-in-the-loop evaluation. • 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. • Ownership of a substantial research direction with strong execution, and experience mentoring others on a fast-moving, applied research team. • Strong coding and experimentation skills (Python, PyTorch/JAX/Tensorflow), and the ability to communicate clearly and align research with product and engineering priorities.