Machine Learning Research Scientist

About the Role

Join a pioneering AI research organization based in Berlin that focuses on creating safe-by-design, non-agentic AI systems. These systems prioritize understanding and forecasting the world rather than executing actions. As a Senior Machine Learning Research Scientist, you will collaborate with a team of researchers, mathematicians, and ML engineers on cutting-edge deep learning, large-scale model experimentation, and evaluation. This position involves actively developing innovative ML methodologies and guiding the technical direction of an innovative AI initiative.

Primary Responsibilities

• Invent and implement novel machine learning techniques to tackle complex research challenges.
• Conduct extensive large-scale experiments and analyze model performance.
• Adapt, fine-tune, and enhance advanced foundation models.
• Establish thorough evaluation strategies and benchmarking standards.
• Interpret experimental outcomes to discover promising new research avenues.
• Engage in close collaboration with a multidisciplinary technical team including researchers and engineers.

Required Qualifications and Skills

• Doctorate (PhD) in Computer Science, Machine Learning, Mathematics, Statistics, or a related discipline.
• Prior publications in leading conferences such as NeurIPS, ICML, ICLR, ACL, EMNLP, or CVPR.
• Between three to eight years of experience in machine learning or deep learning research or related industrial roles.
• Proven experience in hands-on development and training of machine learning models.
• Expertise working with foundation or large language models (LLMs) on a large scale.
• Proficiency in Python and deep learning frameworks such as PyTorch, JAX, or TensorFlow.
• Experience designing experimental setups and assessing ML model behaviors and performance.
• Knowledge of handling distributed machine learning operations or large-scale computational workloads.
• A strong grasp of contemporary deep learning methodologies.
• Of added advantage are contributions to open-source projects and expertise in AI safety, alignment, interpretability, robustness, foundation model research, probabilistic or Bayesian modeling, scientific ML, NLP, model adaptation, or distributed training infrastructure.

Why Join Us?

• Contribute to a fundamentally distinct approach to advanced artificial intelligence focusing on ethical, safe-by-design, non-agentic systems.
• Influence research directions firsthand rather than following a predetermined roadmap.
• Collaborate with an exceptional team combining research, mathematical, and engineering talents.
• Address complex challenges related to deep learning, foundation models, and AI safety.
• Make a tangible impact on safer AI development and scientific advancements.