Senior Model-Steering Scientist for LLM Translation

Senior Model-Steering Scientist for LLM Translation

Full-Time 80000 - 100000 Β£ / year (est.) No working from home possible
The Consensus

At a Glance

  • Tasks: Lead cutting-edge research in model steerability and reinforcement learning for translation models.
  • Company: Join DeepL, a leader in AI-driven translation technology.
  • Benefits: Attractive salary, flexible working options, and opportunities for professional growth.
  • Other info: Fast-paced environment with a focus on innovation and collaboration.
  • Why this job: Make a significant impact on the future of translation with advanced AI technologies.
  • Qualifications: Expertise in machine learning and experience in mentoring teams.

The predicted salary is between 80000 - 100000 Β£ per year.

DeepL is seeking a Senior Research Scientist to lead fine-tuning, post-training, model steerability, and reinforcement learning for the next generation of translation models. You will fuse high-value human data with synthetic data and drive breakthroughs from research to production at scale.

You will work with architectures at hundreds of billions of parameters, building evaluation metrics and promoting reproducibility across the team, while mentoring researchers and engineers in a fast-moving environment.

Senior Model-Steering Scientist for LLM Translation employer: The Consensus

At ClickHouse, we pride ourselves on being an excellent employer that fosters a collaborative and innovative work culture. Our remote-friendly environment in the United Kingdom allows for flexibility while providing ample opportunities for professional growth and development in the field of cloud infrastructure. Join us to be part of a team that values reliability, encourages continuous learning, and offers a supportive atmosphere where your contributions truly matter.

The Consensus

Contact Details:

The Consensus Recruitment Team

We think you need these skills to ace Senior Model-Steering Scientist for LLM Translation

Fine-Tuning
Post-Training Techniques
Model Steerability
Reinforcement Learning
Data Fusion
Evaluation Metrics Development
Reproducibility Practices