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# Research Scientist II/Senior, Discovery Machine Learning

- **Company:** Iambic Therapeutics, Inc
- **Location:** San Diego, CA, United States
- **Experience:** Expert
- **Salary:** $146,400.0 - $203,500.0
- **Contract:** Permanent contract
- **Skills:** Python (Programming Language), Machine Learning
- **Published:** June 17, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=0a9a3541c2c9f089

## About the Role

Do you have experience in Writing skills?, * PhD or equivalent degree in chemistry or another physical science with 3+ years of experience or commensurate degree and job experience. More junior candidates are welcome to apply, title will reflect experience.

 * Familiarity with using statistical analysis techniques to evaluate, troubleshoot, and adapt models in scientific contexts
 * Excellent written and verbal communication skills; must be able to articulate complex scientific concepts, technical challenges, and results clearly and concisely to diverse audiences.
 * Skills: Python programming, statistics, drug discovery

Strongly preferred:

 * PhD or equivalent degree in computational chemistry or machine learning in chemistry
 * 3+ years of experience working in a drug discovery project

## Description

We are seeking skilled scientists to join our Discovery Machine Learning group at Iambic Therapeutics. In this role, you will be a key player in applying advanced ML techniques to support our drug discovery efforts and will be part of drug discovery teams pursuing important therapeutics across multiple indications. The work will include focus on our flagship machine learning models, including Enchant, our multimodal transformer model for predicting drug-target interactions and molecular properties. This role is based at our San Diego headquarters., * Train, fine-tune, and evaluate machine learning models, assessing how they can best be leveraged in a drug-discovery setting and optimizing their performance

 * Partner with computational chemists, medicinal chemists and biologists to leverage ML insights for decision-making in high-throughput design-make-test-analyze cycles
 * Generate value, internally and for partners, from our machine learning models, and collaborate with other discovery teams to shape their use
 * Communicate results with internal drug discovery teams and machine learning scientists and externally with partners
 * Support the evolution of our machine learning models by gathering analytical feedback and delivering domain-driven guidance to the machine learning team


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