Joblaze summary

In the role of Research Scientist II/Senior at Iambic Therapeutics, the individual will focus on training and optimizing machine learning models to enhance drug discovery processes. Key skills include proficiency in Python, statistical analysis, and a strong background in chemistry or machine learning, ideally with experience in drug discovery projects. This position is suited for candidates with a PhD and several years of relevant experience, who can effectively communicate complex scientific concepts to diverse teams. Iambic, a clinical-stage company founded in 2020, emphasizes collaboration among AI experts and drug hunters to drive innovative therapeutic solutions.

Joblaze insights

Listed about 2 months ago — first seen on Joblaze September 3, 2026. Last confirmed on Iambic Therapeutics's careers page November 8, 2026.
Python appears in 49.4% of 468 comparable mid ai/ml roles in United States; drug discovery appears in 0.2% of 468 comparable mid ai/ml roles in United States.

Quick facts

Is the Research Scientist II/Senior, Discovery Machine Learning role remote?: No — this is an on-site role in San Diego HQ.
How much experience is required?: At least 3 years of relevant experience for this Research Scientist II/Senior, Discovery Machine Learning role.
Where is the role based?: Iambic Therapeutics is hiring for this position in San Diego HQ.
What's the tech stack?: Joblaze extracted these technologies from the posting: Python, Statistics, drug discovery.
What seniority level is this role?: Iambic Therapeutics targets mid-level candidates for this position.
Is this full-time or contract?: Full-time for this Research Scientist II/Senior, Discovery Machine Learning role at Iambic Therapeutics.

From the original posting

JOB SUMMARY

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.

KEY RESPONSIBILITIES

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

QUALIFICATIONS

Required:

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

Bold. We are courageous, take thoughtful risks, and approach challenges with tenacity.

Standard company text repeated across Iambic Therapeutics's postings is omitted here.

Research Scientist II/Senior, Discovery Machine Learning

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