Principal / AD / Director, Agentic AI

Iambic Therapeutics is seeking an exceptional scientific and technology leader to lead the design, build, and adoption of agentic AI workflows across small-molecule drug discovery programs, driving efficiency in both internal research and external partnerships. The role will also co-lead a cross-functional initiative to build the Iambic Lab of the Future: an integrated framework in which AI-driven molecular design, automated high-throughput experimental workflows, and real-time data acquisition operate as a unified system. This is a foundational leadership role at the intersection of AI, drug discovery, and experimental science. This will be a hybrid position, based out of the San Diego Headquarters. Iambic is a clinical-stage life-science and technology company developing novel medicines using its AI-driven discovery and development platform. Based in San Diego and founded in 2020.

Responsibilities

Lead the technical roadmap and day-to-day execution for agentic AI workflows that accelerate small-molecule discovery across internal pipeline and external collaborations; Lead the design, build, and deployment of multi-step agentic systems integrating ML-based molecular design (including NeuralPLexer, Enchant and related systems) with automated decision-making loops for hypothesis generation, compound prioritization, and experimental triage; Drive efficiency gains in discovery collaborations, enabling sustainable and scalable business opportunities; Co-lead the creation of the Iambic Lab of the Future alongside experimental leaders, integrating AI molecular design with high-throughput experimental (HTE) platforms and automated data generation pipelines; Partner closely with computational, experimental, and data engineering teams to ensure agentic systems are scientifically grounded, technically robust, and operationally adopted; Represent Iambic's agentic discovery capabilities externally at scientific conferences, partnership discussions, and with academic collaborators; Work closely with senior leadership to develop agentic discovery strategy aligned with platform development, pipeline priorities, and business objectives; Develop and mentor a high-performing team, providing technical guidance and growth opportunities as the agentic AI group scales

Requirements

PhD in computational chemistry, machine learning, structural biology, chemical biology, or a closely related discipline; equivalent experience considered; 8+ years of experience in drug discovery, ML, or platform-building contexts; Demonstrated expertise in designing and deploying AI/ML workflows, ideally in a drug discovery or life sciences context; Experience with LLM-based agents, workflow orchestration frameworks, and integration of ML models into laboratory automation pipelines is strongly preferred; Deep familiarity with small-molecule drug discovery including structure-based design, ADMET, and multi-parameter optimization, combined with hands-on experience applying ML models (e.g., structure prediction, generative chemistry, property prediction) to real discovery programs; Proven track record of leading cross-functional scientific teams and delivering innovative computational or platform capabilities in a fast-paced biotech or pharmaceutical setting; Experience working within or alongside external research collaborations or partnerships, with an understanding of how to adapt workflows and tooling for multi-organizational research environments; Exceptional scientific communication skills; able to translate complex AI/ML concepts for experimental scientists, business development leaders, and external partners

Nice to have

Experience with LLM-based agents, workflow orchestration frameworks, and integration of ML models into laboratory automation pipelines; Experience working within or alongside external research collaborations or partnerships

Benefits

Industry leading competitive pay; Company paid healthcare; Flexible spending accounts; Voluntary life insurance; 401K matching; Uncapped vacation; Onsite gym; Onsite dining