Research Scientist - FMTA

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DeepL is a global communications platform powered by language AI. Since 2017, we’ve been on a mission to break down language barriers. Our human‑sounding translations and intelligent writing suggestions are designed with enterprise security in mind. Today, they enable over 100,000 businesses to transform communications, reach new markets, and improve productivity, and empower millions of individuals worldwide to make sense of the world and express their ideas.

Our goal is to become the global leader in Language AI, building products that drive better communication, foster connections, and make a real‑life impact. Existing products help countless people worldwide and our shared mission to improve communication for individuals and businesses brings cultures closer together. Being part of DeepL means joining a team dedicated to innovation and employee well‑being.

Meet the team behind this journey:
Foundation Model Task Adaptation. We are the team behind DeepL’s post‑training stack for large language models. We focus on developing algorithms and systems that align pre‑trained models with tasks and performance goals through techniques like reinforcement learning. As a research‑driven team, we stay up to date with current literature to integrate cutting‑edge ideas into our core stack. As part of this team, you will shape the future of how our models learn beyond pre‑training: enabling new capabilities, better cont rollability, and safer, more effective user experiences.

As a Research Scientist, you’ll design, implement, and deploy cutting‑edge research in reinforcement learning and post‑training at scale, driving innovations that make it into production.

• Build and deploy state‑of‑the‑art reinforcement learning pipelines at scale.
• Post‑train large (multi‑modal) models to align them with human intent and enable general capabilities such as reasoning, pushing the boundaries of model performance, safety, and efficiency.
• Always keep the entire lifecycle of research and production in mind: from idea conception, theoretical modeling, prototyping, ablation studies, all the way to production deployment.
• Build and foster external collaborations with academic and industrial partners.
• Follow scientific and technical standards for experimentation, reproducibility, and model evaluation.
• Collaborate deeply with Engineering, ML Platform, and HPC teams to deliver robust and reliable model updates to users.

• We are seeking researchers with a strong practical background, a creative mindset, and a passion for solving hard problems with real‑world impact.
• You have a solid mathematical background and enjoy solving challenging problems, evidenced by a master’s degree, diploma, PhD, or equivalent industry experience in mathematics, physics, computer science, or a related field.
• Deep practical experience in Python and at least one modern machine learning framework such as PyTorch, Tensor Flow, or JAX; experience working with large compute clusters and ML infrastructure is a plus.
• A track record of leading self‑directed research projects that go well beyond academic exercises and deliver tangible results.
• Expertise in deep reinforcement learning (RLHF/RLAIF/RLVR) is a plus.
• Hands‑on experience scaling and deploying LLMs or other foundation models in real‑world systems is a plus.

• Diverse and internationally distributed team: joining our team means becoming part of a large, global community with people of more than 90 nationalities. Our global presence is growing–we've doubled in size nearly every year, with our employees based in the UK, Germany, the Netherlands, Poland, the US, and Japan, and we continue to expand our network.
• Open communication, regular feedback: as a language‑focused company, we value the importance of clear, honest communication. We value smooth collaboration, direct and actionable feedback, and believe that leading with empathy and growth mindset makes us better together.
• Hybrid work, flexible hours: we offer a hybrid work schedule, with team…