Senior Research Scientist | Multimodal Systems

DeepL; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

DeepL in London is seeking a Senior Research Scientist to lead fine-tuning, post-training, and RLHF efforts for our 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 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

Qualifications

• Proven track record in developing multimodal models and vision models.
• Hands-on expertise in post-training, knowledge distillation or RLHF/RLAIF.
• Strong data-centric instincts for synthetic data and evaluation pipelines.
• A hands-on builder who can train models and ship to production.

Responsibilities

• Lead development of vision and multimodal models for document translation.
• Drive post-training, distillation, and RLHF for models.
• Build evaluators and reduce reward hacking in production.
• Own full model delivery lifecycle from prototyping to deployment.
• Establish practices for evaluation, reproducibility, and monitoring.

Skills

Multimodal modelling

Post-training

Reinforcement learning

Python

PyTorch

Experimentation

Tools

PyTorch

JAX

TensorFlow

Job description

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.

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.