Job Description

Octant is pioneering a new generation of precision medicines by combining synthetic biology, chemistry, and AI/ML to tackle complex cellular mechanisms driving human disease. We are a small molecule therapeutics company scaling drug discovery to unlock therapies for genetically defined and historically intractable diseases.

JOB DESCRIPTION

We’re looking for a computational drug discovery intern to join Octant this summer in a program funded by the Gates Foundation to identify small-molecule drugs targeting HPV-driven cancers. In this role, you’ll work alongside our computational and experimental teams to build and iterate on machine learning models, explore molecular representations and structure-activity relationships, and help drive compound design and prioritization from data to decision.

THIS INTERNSHIP MIGHT BE GREAT FOR YOU IF YOU ARE/HAVE:

• Currently enrolled in or recently completed a BS or MS in a quantitative field (CS, bioinformatics, applied math, data science, computational biology, or adjacent)
• Proficient in Python for data analysis and scripting (pandas, numpy, scikit-learn at minimum)
• Experience building or training ML models on real datasets, not just coursework exercises
• At least one substantive research experience (academic lab, industry internship, or independent project) where you drove a project from question to result
• Familiarity with version control (git) and working in shared codebases
• Coursework or research exposure in at least one biological science area
• Ability to communicate results to both computational and experimental audiences
• Comfort with ambiguity and fast iteration. The project work is on a weekly cadence, which means making judgment calls with incomplete data, not waiting for perfect information

EVEN BETTER IF YOU HAVE:

• Hands-on experience with molecular representations (SMILES, fingerprints) or cheminformatics toolkits (RDKit, DeepChem)
• Experience with cloud/distributed compute environments (Databricks, Spark, or similar)
• Understanding of structure-activity relationships, compound libraries, or hit expansion concepts
• Demonstrated ability to work across the computational-experimental boundary (e.g., designed an analysis that informed a wet-lab decision, or interpreted assay data to guide a model)
• Familiarity with active learning or Bayesian optimization frameworks
• Experience building or working with agentic systems, LLM tool-use pipelines, or multi-step automated workflows
• Familiarity with prompt engineering and structured output parsing from language models
• Experience with workflow orchestration or pipeline tools (Databricks, Airflow, or similar)
• Comfort designing and consuming APIs or integrating across multiple tools/platforms programmatically
• Experience with software engineering best practices beyond scripting (testing, error handling, logging, modular code design)
• Familiarity with database interactions (SQL, querying structured data stores) in a production or semi-production context

Optional: Along with your application, please share a paper/preprint/software repo that best highlights your strengths so we can better understand the work you’ve led. If there is nothing public, please summarize that work in a few paragraphs. 

The pay range for this role is $1,400 to $1,500 per week, depending on experience. The duration of the internship is up to 10 weeks.

Octant is located in Emeryville, California and we work onsite.