Staff/Principal Applied ML Scientist - Drug Discovery

genesis.ml; San Mateo (CA); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

Salary and equity

Health benefits

401(k) plan

Unlimited PTO

Free meals at offices

Paid family leave

Disability insurance

Life insurance

Job summary

Genesis Molecular AI is seeking a senior computational scientist to bridge long‑term research and active drug discovery programs. You will build, evaluate, monitor, and deploy state‑of‑the‑art models within real projects, guiding discovery of new medicines.

You will translate research insights for experimental teams, ensure model quality, and collaborate across chemistry, biology, and ML to push the frontier of AI-driven drug design.

Qualifications

• PhD in Cheminformatics, Computational Chemistry, Computer Science or related field.
• Track record of applying ML to drug discovery challenges.
• Experience with graph neural networks, multitask modeling, active learning, or Bayesian optimization.

Responsibilities

• Assess model performance and utility with project teams and ML/engineering groups.
• Assist experimental colleagues with interpretation of model predictions and uncertainty.
• Validate predictions against project data and internal/external benchmarks.
• Curate internal and external datasets for model training and validation with experimental teams.
• Design and analyze experiments on model changes and alternative architectures.

Skills

Deep learning

Python

PyTorch

Cheminformatics

RDKit

OpenEye

Data science

Uncertainty quantification

Education

PhD in Cheminformatics/Computational Chemistry/CS

Tools

RDKit

OpenEye

SQL databases

Data pipelines

PyTorch

scikit-learn

Job description

Genesis Molecular AI is seeking a senior computational scientist to bridge long‑term research and active drug discovery programs. You will build, evaluate, monitor, and deploy state‑of‑the‑art models within real projects, guiding discovery of new medicines.

You will translate research insights for experimental teams, ensure model quality, and collaborate across chemistry, biology, and ML to push the frontier of AI-driven drug design.