Jobs at Iambic Therapeutics

The Machine Learning Scientist will develop and scale a multimodal transformer model for drug discovery, optimizing training and collaborating across disciplines.

Lead the Cloud Ops roadmap, design AWS infrastructure, automate with Infrastructure as Code, mentor teams, and ensure compliance with ISO 27001.

Design and build automated systems for data acquisition, cleaning, and quality control for biomedical data. Collaborate with ML scientists and maintain data pipelines.

Own end-to-end equity plan administration (options, RSUs, ESPP), cap table reconciliation, and equity platform management. Partner with Legal, Finance, and HR on program design, SEC and exchange compliance, insider trading administration, transfer agent relationships, and corporate governance. Build scalable processes, support SEC filings from an equity data perspective, develop employee compliance training, and identify automation and process improvements.

Provide hands-on corporate legal support: draft and negotiate commercial agreements, manage corporate governance, support securities reporting and financing workstreams, implement legal templates and AI-enabled workflows, advise cross-functional stakeholders, and coordinate outside counsel.

Apply and optimize advanced ML models (including multimodal transformers) to support drug discovery. Collaborate with chemists and biologists, evaluate models, integrate ML insights into design-make-test-analyze cycles, communicate results internally and externally, and guide ML model evolution with domain-driven feedback.

Partner with Discovery R&D teams to lead FP&A activities including annual planning, reforecasting, budgeting, financial modeling, headcount planning, and program-level budgets. Provide actionable financial insights to leadership, support month-end close and accruals, assist long-range and scenario planning, and develop scalable FP&A tools and processes while performing ad hoc analyses and supporting business development negotiations.

The role involves integrating and operating compute infrastructure for data processing and model inference, ensuring reliability and scalability, and collaborating with ML engineers.

Lead the DMPK function by defining scientific strategies, collaborating with teams to execute studies, and ensuring regulatory compliance while managing research initiatives and partnerships.

Build, deploy, and operate production ML systems for drug discovery, including training/finetuning, inference, evaluation, GPU-accelerated services, CI/CD, observability, cloud deployment, and collaboration with ML scientists and cross-functional teams.