Head of AI & Computational Biology
RF Hiring Solutions, Boston, MA, United States
Company Stage: Venture-Backed Biotechnology Startup
Focus: Stem Cell Engineering, Regenerative Medicine, Translational Platforms
The Opportunity
We are building a next-generation stem cell platform company leveraging AI to accelerate cell line engineering, differentiation control, and therapeutic development. Our mission is to shorten the path from biological insight to clinical impact.
We are seeking a Head of AI & Computational Biology to architect and scale the company’s AI-driven discovery engine. This leader will integrate machine learning, multimodal biological data, and experimental feedback loops to optimize stem cell development and translational outcomes.
This is a foundational executive role reporting to the CEO, partnering closely with Wet Lab, Process Development, and Regulatory teams.
What You’ll Build
- AI/ML models for stem cell differentiation prediction and optimization
- Multimodal integration across single-cell RNA-seq, spatial transcriptomics, epigenomics, and imaging datasets
- Predictive QC and batch consistency models for GMP-scale production
- Scalable data infrastructure compliant with FDA and GxP environments
Core Responsibilities
- Define and execute the company’s AI and computational biology strategy
- Lead a team of ML engineers, computational biologists, and data scientists
- Develop foundation models for cellular state prediction and lineage mapping
- Partner with R&D to translate models into validated biological insights
- Implement MLOps and data governance standards suitable for regulated biotech
- Present technical progress to board members and investors
Required Background
- PhD in Computational Biology, Bioinformatics, Machine Learning, Systems Biology, or related field
- 8+ years experience applying ML to biological systems
- Deep expertise in single-cell data analysis and high-dimensional biological datasets
- Proven experience building and leading high-performance technical teams
- Experience in venture-backed biotech or translational environments preferred
Technical Stack Experience
- Python (PyTorch, TensorFlow, JAX)
- Single-cell toolkits (Scanpy, Seurat, CellRank)
- Cloud infrastructure (AWS/GCP/Azure)
- ML Ops frameworks
What Success Looks Like (12–24 Months)
- AI-driven improvements in differentiation efficiency and reproducibility
- Reduced experimental iteration cycles by >30%
- Published or patentable computational discoveries
- A scalable, defensible AI platform integrated with laboratory operations