Machine Learning and Molecular Simulation Scientist
AI-focused biotechnology company developing molecular-AI systems and small-molecule drug candidates for internal programs and pharmaceutical partners.
Funding history
About Genesis Molecular AI
Genesis Molecular AI combines foundation models, generative and predictive AI, and physics-based computation in its GEMS platform for small- and medium-size-molecule drug discovery.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will develop and apply 3D molecular simulation and machine learning methods for active drug discovery programs. You will integrate physics-based and data-driven approaches, run simulations, improve molecular scoring capabilities, collaborate with discovery scientists, implement new research methods, and communicate scientific results.
Requirements
- 3D machine learning
- Geometric deep learning
- Graph neural network
- Equivariant neural network
- Diffusion model
- Molecular simulation
- Molecular dynamics
- Enhanced sampling
- Free energy calculation
- GROMACS
- AMBER
- OpenMM
- NAMD
- Structure-based drug design
- Docking
- Binding-site analysis
- Protein-ligand interaction modeling
- MOE
- PyMOL
- Python
- PyTorch
- JAX
- NumPy
- MDAnalysis
- RDKit
- HPC
Responsibilities
- Build and apply ML models using 3D structural data
- Integrate physics-based and data-driven approaches
- Develop molecular dynamics, enhanced-sampling, and free-energy simulation methods
- Improve generative AI, scoring functions, and force fields
- Apply computational methods across the drug discovery pipeline
- Implement and adapt methods from current literature
- Communicate scientific results to multidisciplinary teams
Benefits
- Bonus
- Equity
- Medical insurance
- Dental insurance
- Vision insurance
- Stock options
- 401(k) plan
- Open PTO
- Paid company holidays
- Daily office meals and snacks
- Flexible work environment