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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. This foundational leadership role sits at the intersection of AI, drug discovery, and experimental science, driving efficiency in both internal research and external partnerships.
You will lead the technical roadmap and day-to-day execution for agentic AI workflows that accelerate small-molecule discovery across Iambic's internal pipeline and portfolio collaborations. Key responsibilities include designing, building, and deploying multi-step agentic systems that integrate ML-based molecular design (including NeuralPLexer, Enchant, and related systems) with automated decision-making loops for hypothesis generation, compound prioritization, and experimental triage.
You will co-lead the creation of 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 requires close partnership with computational, experimental, and data engineering teams to ensure agentic systems are scientifically grounded, technically robust, and operationally adopted across discovery programs.
Additionally, you will represent Iambic's agentic discovery capabilities externally at scientific conferences and in partnership discussions, serve as a thought leader in AI-driven drug discovery, and develop and mentor a high-performing team as the agentic AI group scales. You will work closely with senior leadership to align agentic discovery strategy with overall platform development, pipeline priorities, and business objectives.
Iambic is a clinical-stage biotech company founded in 2020, based in San Diego, with a world-class team uniting pioneering AI experts and experienced drug hunters. The platform has demonstrated delivery of new drug candidates to human clinical trials with unprecedented speed across multiple target classes and mechanisms of action.
QUALIFICATIONS:
- PhD in computational chemistry, machine learning, structural biology, chemical biology, or 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 drug discovery or life sciences context
- Experience with LLM-based agents, workflow orchestration frameworks, and integration of ML models into laboratory automation pipelines (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 (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 fast-paced biotech or pharmaceutical setting
- Experience working within or alongside external research collaborations or partnerships, with 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
