Applied ML Scientist (Staff

About the role

We are seeking a skilled scientist to drive the application of advanced AI techniques to address significant challenges in drug discovery. In this pivotal position, you will connect our long-term research initiatives with our active drug development efforts, focusing on the enhancement and deployment of our innovative models to facilitate the discovery of new therapeutic options.

Key facts

What you'll do

• Collaborate with project teams to evaluate the performance and relevance of models, ensuring alignment with current project objectives while working with ML and engineering teams to troubleshoot and enhance functionalities.
• Support experimental teams by interpreting model predictions and providing insights into model reliability and uncertainty.
• Validate model predictions against project-specific data and established benchmarks to assess quality.
• Work with experimental teams to curate datasets for training and validating models.
• Participate in the design and analysis of experiments assessing model modifications and alternative architectures.

Requirements

• Extensive experience as a computational scientist with a history of applying machine learning methods to influence small molecule drug discovery projects.
• Expertise in cheminformatics, with proficiency in molecular data tools such as RDKit or OpenEye.
• Familiarity with experimental drug discovery processes, including various assay types (biochemical, binding, cell-based, in vivo) and CADD workflows (docking, virtual screening, ADME prediction).
• Strong data science background, with experience in modeling and analyzing small molecule datasets, and a focus on statistical validation and uncertainty quantification.
• Proficient in Python programming and knowledgeable in applied machine learning frameworks (e.g., scikit-learn, PyTorch).
• Excellent communication skills, capable of bridging the gap between machine learning researchers and experimental scientists.
• A problem-solving mindset, eager to explore the intersection of AI, physics, chemistry, and biology to contribute to foundational discoveries.
• A collaborative spirit, thriving in environments that integrate science and engineering.

Nice to have

• A PhD in Cheminformatics, Computational Chemistry, Computer Science, or a related discipline, along with a history of publications in machine learning applications for drug discovery.
• Advanced modeling expertise, including graph neural networks, multitask modeling, active learning, or Bayesian optimization.
• Experience with large-scale data management, including SQL databases and data pipeline tools.
• Strong perspectives on molecule featurization and model validation.

Skills & tools

• Python, scikit-learn, PyTorch, RDKit, OpenEye, cheminformatics tools, data management systems.

Practical notes

• Competitive salary and equity package.
• Comprehensive health benefits including medical, dental, and vision coverage at 100% for employees.
• 401(k) retirement plan.
• Unlimited paid time off policy.
• Complimentary meals at the office.
• Paid family leave for both maternity and paternity.
• Life insurance and short- and long-term disability coverage.

About Genesis Molecular AI

Genesis Molecular AI is at the forefront of developing foundational models for molecular AI, aiming to transform drug design and development. Our generative AI platform, GEMS, combines AI and physics to create leading models for drug molecule generation and optimization, including the innovative Pearl model for structure prediction. Backed by top investors in AI and life sciences, we have established significant partnerships in the pharmaceutical sector, including a major collaboration with Incyte. Our headquarters is located in San Mateo, CA, with a fully equipped laboratory in San Diego. We are committed to fostering an inclusive workplace and are proud to be an Equal Opportunity Employer.