Machine Learning Scientist

To advance AI-driven drug discovery, the full-time hybrid Machine Learning Scientist will research and develop post-training methods for large multimodal transformer models, focusing on enhancing their capabilities in biomedical applications. Key Responsibilities Research and develop post-training strategies for large-scale multimodal foundation models Design reward functions, training objectives, and evaluation protocols for reinforcement learning and other post-training approaches Collaborate with cross-functional teams to productionize models and ensure alignment with drug discovery needs Required Qualifications PhD in machine learning, computer science, computational chemistry, physics, or a related computational STEM field, or equivalent industry experience Strong Python and PyTorch skills, including experience with training and evaluating deep learning models Demonstrated experience training large-scale transformer models Experience with reinforcement learning approaches or systematic hyperparameter optimization Comfort with modern ML infrastructure such as Docker, CUDA, and Kubernetes