Senior Research Scientist | Multimodal Systems
- Role
- AI / ML
- Experience
- Senior
- Employment
- Full-time
- Company size
- Enterprise
Open to GB only. Set where you work from to check your eligibility.
No BS summary
Senior research scientist for multimodal, VLM and vision models in document translation. Needs hands-on post-training, knowledge distillation and/or reinforcement learning experience plus Python and PyTorch/JAX/TensorFlow. Must be based in the United Kingdom.
Core skills
Required skills
Optional skills
Meet DeepL
DeepL is a global AI product and research company focused on building secure, intelligent solutions to complex business problems. Over 200,000 business customers and millions of individuals across 228 global markets today trust DeepL's Language AI platform for human-like translation, improved writing and real-time voice translation. Founded in 2017 by CEO Jaroslaw “Jarek” Kutylowski, DeepL now has around 1,000 passionate employees and is supported by world-renowned investors including Benchmark, IVP, and Index Ventures.
Our goal is to become the global leader in trusted, intelligent AI technology, building products that drive better communication, foster connections, and create a meaningful impact. To achieve this, we need talented people like you to join our journey. If you’re ready to shape the future of AI and grow your career in a fast-moving, purpose-driven environment, DeepL is your next destination.
What sets us apart
What sets us apart is our blend of cutting-edge AI technology, meaningful work, and a culture where people truly thrive. We’re a team of innovators, researchers, and creators driven by a shared purpose to unlock human potential by making work simpler, smarter, and more connected.
When we share what it’s like to work at DeepL, the reactions are overwhelmingly positive. This might be because of our technology that helps millions of people and businesses communicate and work better every day, or because of the trust, curiosity, and care that shape our culture.
What we know for sure is this: being part of DeepL means joining a team dedicated to innovation, growth, and well-being. Discover more about life at DeepL onLinkedIn,Instagram, and our Blog.
Meet the team behind this journey: Language AI
Our Language AI teams form the foundation of DeepL's success. We are a dedicated group of researchers who collaborate closely with engineers, product managers, and designers. Our mission is to build the world's leading language AI system to deliver perfect translations for the most demanding use cases. To that end, we take responsibility for the entire life cycle of the machine learning models that power our language AI products. This includes data, training, quality assurance, and operational aspects. In our highly collaborative teams, each person has the scope to drive impact across the company.
Your responsibilities
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.
You will:
- 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.
Nice to have
- 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.
#LI-JB1
We are an equal opportunity employer
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.
What you'll do
- Lead fine-tuning, post-training, and reinforcement learning for DeepL's next-generation document translation multimodal and vision models.
- Own a major research direction, prototype rapidly, run large-scale experiments, and drive breakthroughs into production.
- Develop models that reason about document layout by fusing expert, real-world and synthetic data.
- Lead efforts to make translation highly steerable and adaptable.
- Drive development of vision and multimodal models for document, image and media translation, from media ingestion and generation to end-to-end models.
- Drive hands-on research and development on post-training for vision and/or multimodal models, including 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.
- 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.
- Work closely with engineering to ship models into real-time systems at scale.
- Establish strong practices for evaluation, reproducibility, monitoring, and continuous model improvement in production.
What they require
- 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, including 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.
- 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, including Python and PyTorch/JAX/TensorFlow.
- Ability to communicate clearly and align research with product and engineering priorities.
- Ability to lead complex research efforts, communicate clearly and collaborate across teams while staying grounded in product impact, user experience, and real-world performance.
- Preferred: Experience with machine translation, multilingual NLP, efficient long-context modeling, language quality estimation, or multimodal machine translation.
- Preferred: Experience designing evaluation and reward signals using automatic metrics, Model-as-judge evaluation, non-verifiable rewards, and human-in-the-loop evaluation.
- Preferred: Experience with multi-objective optimization, consistency models, unified multimodal generation.
- Preferred: Experience with diffusion models.
- Preferred: Publications at top-tier venues.
Benefits
- Meaningful work with cutting-edge AI technology.
- Culture focused on trust, curiosity, care, innovation, growth, and well-being.
- Opportunity to help build trusted AI technology that improves communication and connection.
- Scope to drive impact across the company.
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