Senior AI Research Scientist
- Lead the design and the technical implementation of novel ML algorithms with a strong focus on deep learning, generative models, graph neural networks, and physics-informed AI to predict molecular properties and protein-ligand interactions.
- Take part in cutting-edge research and bibliographic exploration. You'll identify relevant recent publications, benchmark promising ideas, and propose model or pipeline improvements.
- Act as a mentor to junior team members, offering guidance on system design, model implementation, debugging, and good coding practices.
- Collaborate with AI researchers, computational chemists, and drug discovery teams to translate AI models into actionable scientific insights.
- Be deeply involved in coding, from prototyping to deploying production-ready models. You should have a strong software engineering mindset and a passion for writing clean, efficient, and scalable code.
- Share best practices in deep learning model development, testing, and reproducibility within the team.
- Scale AI models for real-world applications, optimizing model efficiency, interpretability, and generalization to unseen chemical spaces.
- Strong problem-solving skills, autonomy and a collaborative mindset.
- PhD or equivalent practical experience in Machine Learning, Computer Science, Applied Mathematics or a related field, with at least 8 years of experience.
- Strong programming skills in Python and proficiency with Deep Learning frameworks (e.g., PyTorch, JAX, TensorFlow).
- Demonstrated hands-on experience implementing and deploying deep learning models in production or research environments.
- Strong scientific rigor, curiosity, and ability to perform bibliographic research and exploratory R&D.
- Experience in drug discovery, chemistry, or physics.
- Experience with physics-informed ML models.
- Familiarity with high-performance computing (HPC) and cloud-based ML pipelines.
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