Machine Learning Scientist — Large Multimodal Models (Post-Training)

Skills

About the Role

You will research and develop post-training strategies for large multimodal foundation models. You will design reward functions, training objectives, data-generation approaches, evaluation protocols, experimentation workflows, and inference optimizations. You will build benchmarking systems, productionize models and services, collaborate on drug-discovery applications, communicate results, and write tested, documented research and engineering code.

Requirements

• PhD in machine learning, computer science, computational chemistry, physics, or a related computational STEM field, or equivalent industry experience
• Python
• PyTorch
• Experience implementing, training, debugging, and evaluating deep learning models
• Experience training large-scale transformer models
• Experience with reinforcement learning or fine-tuning methods
• Hyperparameter optimization or large-scale experimentation
• Reproducible experimentation, clean code, testing, and performance-aware debugging
• Docker
• CUDA
• Kubernetes
• Experiment tracking

Responsibilities

• Research and develop post-training strategies for multimodal foundation models
• Design reward functions, training objectives, data-generation strategies, and evaluation protocols
• Build experimentation and hyperparameter-optimization workflows
• Develop inference-optimization techniques for evaluation and discovery workflows
• Design and maintain benchmarking and evaluation frameworks
• Collaborate to productionize models, evaluation systems, and inference services
• Partner with scientific specialists to ground model development in drug-discovery needs
• Communicate results to internal teams, external partners, and conferences
• Write, test, document, and package machine learning components

Benefits

• Private medical insurance
• Life assurance
• Pension contributions
• Flexible holiday allowances