Principal Agentic Scientist

About the Opportunity: A fast-growing, AI-first, venture organization focused on accelerating pharmaceutical R&D is seeking a Principal Scientist to architect and lead agentic AI/ML systems across the drug discovery space, from target ID through translational and clinical development. This is a chance to build the foundation of LLM-based agents and agentic workflows, that ties genomics, protein design, multi-omics, cheminformatics, and literature mining into unified, end-to-end pipelines scientists rely on daily.

The Role: Lead the design, orchestration, and scaling of agentic AI systems that automate and accelerate computational biology workflows. Partner with venture and platform leadership to define pragmatic AI strategy, own technical execution, and set the benchmarking and evaluation bar for agentic systems. What You'll Own Program Leadership: Lead multiple AI/ML and computational programs spanning preclinical, translational, and clinical R&D. Agentic System Architecture: Design, build, and scale agentic systems (Lang Graph, CrewAI, Auto Gen, Pydantic AI) that orchestrate ML and comp-bio tools — genomics, biomolecule design, docking, multi-omics, literature mining — into automated end-to-end scientific workflows. Technical Ownership: Own build, benchmarking, evaluation, and maintenance of agentic pipelines, including sandboxed code execution, agent harness development, and observability/cost-governance tooling. Team Leadership: Manage and mentor scientists/engineers; support recruiting and interviewing as the ML function grows. Strategy & Landscape: Track emerging agentic-AI/ML literature; translate it into build strategies that accelerate R&D. Communication: Translate complex technical work for cross-functional and executive audiences to drive decisions.

What You Bring: MS or PhD in Machine Learning, Statistics, Computational Biology, or a related field, with 5+ years applying AI/ML in academic, pharma, or biotech settings. Demonstrated ability to lead agentic AI/ML projects end-to-end, from architecture to production. Strong Python and modern ML framework depth (PyTorch or JAX/Tensor Flow). Track record of publications, patents, or high-impact technical delivery.

Preferred: hands‑on integration of LLM platforms (Anthropic, OpenAI, Vertex AI, Bedrock) and evaluation/feedback‑loop frameworks for agentic systems; breadth across genomics, protein design, proteomics, cheminformatics/ADMET, or biophysics.