Back to search
Capabilities:
- Masters or PhD in Computational Chemistry (e.g., Cheminformatics or Quantum Mechanics) or Artificial Intelligence applied to scientific problem-solving.
- Proven track record of impactful contributions to scientific projects.
- Advanced proficiency in Python or experience with other programming languages (e.g., Java, C/C++).
- Desire to rapidly transition novel chemical AI research into production environments to transform drug discovery.
Expertise in one or more of the following areas:
- Artificial Intelligence: Experience with state-of-the-art methods such as graph neural networks, transformers, Gaussian processes, generative modeling, or reinforcement learning.
- Cheminformatics: Knowledge of chemistry data storage, formats, and synthesis prediction; proficiency with toolkits such as RDKit or OpenEye.
- Quantum Mechanics: Experience applying QM techniques to synthesis prediction, including using QM toolkits (e.g., PSI4, Orca, Gaussian).
- Experience with data curation and processing from heterogeneous sources; familiarity with tools like Apache Spark or Hadoop.
- Proficiency with cloud platforms (AWS, GCP, Azure).
- Familiarity with major machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
- Open-source contributions or publications demonstrating expertise in machine learning for scientific applications.
- Hands-on experience with best software development practices in collaborative environments.
If interested, please apply now.
