Meet DeepL

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. To achieve this, we need talented individuals like you to join our exciting journey. If you're ready to work with a dynamic team and build your career in the fast-moving AI space, DeepL is your next destination.

What sets us apart

What sets us apart is our blend of modern technology, competitive benefits, and an open, welcoming work culture that enables our people to thrive. When we share what it's like to work at DeepL, the reactions are overwhelmingly positive. This may be because of our products that have helped countless people worldwide or our shared mission to improve communication for individuals and businesses, bringing cultures closer together. What we know for sure is this: being part of DeepL means joining a team dedicated to innovation and employee well-being. Discover what our teams have to say about life at DeepL on LinkedIn, Instagram and our Blog.

Senior Research Scientist - FMTA

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 controllability, and safer, more effective user experiences.

Your responsibilities

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

You will:

• 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

Qualities we look for

We’re looking for a scientist with a deep technical background, strong leadership skills, and a proven track record of driving research in reinforcement learning or large-scale model alignment to production.

• 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 masters 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, TensorFlow, 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.

What we offer

• Diverse and internationally distribu