Principal Scientist, Biologics Machine Learning

• Lead several AI/ML and computational projects across early-stage ventures, from scoping through production, within agreed budgets and timelines.
• Own method and pipeline development for protein modeling and design, including generative modeling (diffusion, flow, VAEs) for sequences, graphs, or 3D structures.
• Design LLM-based agents and agentic workflows that automate protein design, docking, and mass spec pipelines.
• Drive benchmarking, scaling, and implementation into production-grade systems, with rigor in model development and reporting.
• Manage and coordinate scientists and engineers, mentor early hires, and support recruiting and interviewing.
• Scout emerging literature, propose new development strategies, and represent the team to portfolio companies and external partners as a recognized subject matter expert.

• Master’s or PhD in a relevant field (machine learning, mathematics, statistics, computational sciences) with 5+ years of scientific, engineering, or computational experience in academic, pharmaceutical, or biotech settings; industry AI/ML experience preferred.
• Depth in Python, modern ML frameworks (PyTorch or JAX/TensorFlow), and deep learning architectures.
• Experience driving results through teams of engineers and scientists in fast-paced, entrepreneurial environments.
• Track record of leadership on AI/ML model progression, reflected in publications, patents, or high-impact internal reports., * Hands-on protein modeling and design, and LLM/agentic automation of scientific workflows.
• Breadth across proteomics/mass spec, cheminformatics/docking/ADMET, and biophysics/MD.
• Generative modeling (diffusion/flow/VAEs) for sequences, graphs, or 3D structures; docking rescoring (e.g., gnina, DiffDock) and pose quality metrics.

Our client is an AI-first venture builder that creates and scales new companies in human health, sustainability, and beyond, applying recent advances in machine learning and data to accelerate fundamental research.

Lead AI/ML projects at the intersection of protein design and agentic automation across early-stage ventures. This is a hands-on scientific leadership role for a self-directed “serial deep diver”: someone who can build generative and structure-based methods for designing biomolecules, then spin up LLM-based agents that automate the scientific workflows around them. You will set technical direction, work directly with founders and platform engineers, and turn one-off wins into shared libraries adopted across the portfolio.