Quantum Cheminformatics Scientist
| Company | PsiQuantum |
|---|---|
| Location | Palo Alto, CA, USA |
| Salary | $140000 – $175000 |
| Type | Full-Time |
| Degrees | PhD |
| Experience Level | Junior, Mid Level, Senior |
Requirements
- Ph.D. in cheminformatics, computational chemistry or physics, chemical or materials engineering, or closely related fields, with a strong focus on ML methodologies for molecular modeling or materials discovery, and 0 to 6 years of post-PhD (postdoctoral or industrial) experience.
- Hands-on experience developing and applying machine learning models to solve real-world problems in cheminformatics, drug design, or materials science.
- Expertise in Python programming and familiarity with scientific libraries and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
- Demonstrated ability to develop computational workflows integrating ML and molecular modeling tools.
- Published peer-reviewed articles in the field of molecular modeling, applied machine learning, or cheminformatics.
Responsibilities
- Conduct innovative research and develop workflows that combine quantum computing and ML-driven cheminformatics for molecular modeling and property prediction.
- Develop and apply machine learning models to accelerate molecular and materials discovery in areas such as drug design, catalysis, and energy materials.
- Collaborate with quantum algorithm experts to identify areas where quantum computing can have the greatest impact in cheminformatics and materials discovery.
- Act as a technical lead in collaborative projects, working with internal and external teams to integrate quantum and ML-driven insights into cheminformatics workflows.
- Serve as a subject matter expert in ML-driven cheminformatics, staying updated on recent advancements in AI, computational chemistry, and quantum computing.
- Document and communicate research findings through internal reports, peer-reviewed publications, and conference presentations.
Preferred Qualifications
- Experience with molecular modeling techniques, including quantum chemistry methods (e.g., DFT, coupled cluster theory, or wavefunction-based methods), molecular dynamics, or machine learning potentials.
- Familiarity with techniques for property prediction, structure-to-function modeling, or molecular fingerprint generation.
- Hands-on experience with GPU-accelerated ML workflows or high-performance computing for cheminformatics applications.
- Exposure to generative AI (e.g., large language models) and its applications in chemistry or materials science.
- Experience applying quantum computing or hybrid quantum-classical approaches to cheminformatics problems.