Senior Research Scientist | Model Scaling

As Senior Research Scientist at DeepL, you will own foundational modelling decisions behind next-generation translation models. You will prototype rapidly, run large-scale experiments, and drive architecture choices for scaling open models, working closely with post-training, RL, and instruction-following experts. You’ll influence which foundation models to build on and how to adapt base models for production-ready translation systems. Join a globally distributed, innovation-driven team shaping the future of language AI.

• Hybrid work
• Virtual Shares
• 30 days of annual leave
• Regular in-person team events
• Monthly hack Fridays
• Competitive location-aware benefits

• Drive selection and evaluation of open foundation/open-weight models for next-generation translation systems
• Lead model selection and architecture decisions to scale to hundreds of billions of parameters (including Mixture-of-Experts and sparse/efficient designs)
• Design multi-capability adaptation strategies using LoRA, PEFT, and related methods
• Own the modelling lifecycle: prototyping, ablations, scaling experiments, evaluation, and production delivery
• Collaborate with post-training, RL/RLHF, and instruction-following specialists to align base models with product goals
• Stay ahead of open-model and scaling literature and provide well-founded recommendations to the team

• Hands-on experience adapting and scaling large language models via fine-tuning/instruction-tuning/post-training of multi-billion-parameter models
• Sound judgment on architecture trade-offs at scale (dense vs. MoE) and choice of open-weight foundation models
• Knowledge of parameter-efficient and multi-capability adaptation (LoRA/PEFT) and variants
• Hands-on model training, experimentation, and debugging pipelines with production-ready results
• Strong coding and experimentation skills (Python, PyTorch/JAX/TensorFlow)
• Clear communication and cross-team collaboration to align research with product and engineering priorities

• Strong communication
• Collaborative mindset across teams
• Problem-solving and debugging mindset
• Python
• PyTorch
• JAX