Overview In this role you will lead the development of polyintelligent AI systems that fuse human biology expertise with machine intelligence to enable multi-modal, multi-scale reasoning for biology. You will guide architecture, data exploration, benchmarking, and integration of reasoning engines into the AI Scientist platform to advance autonomous scientific workflows. You will identify high-value use cases across Flagship and portfolio companies and translate biological needs into technical plans. You will work with cross-functional teams to translate discoveries into real-world biological solutions at scale. This is an opportunity to shape a new capability that could transform venture creation and
Compensation / Benefits- healthcare coverage
- annual incentive program
- retirement benefits
- broad range of other benefits
Responsibilities- Own the roadmap from architecture through model readiness, benchmarking, and integration
- Guide multi-modal, multi-scale model design with a biological lens for mechanism-of-action, target discovery, perturbation biology, pathways, protein function, and autonomous workflows
- Translate biology into model requirements and evaluation criteria for real scientific workflows
- Build and integrate reasoning engines into the AI Scientist platform for autonomous science
- Source and shape portfolio use cases by engaging with Flagship teams and end-users to define success criteria and data assets
- Communicate strategic direction and scientific results to internal stakeholders and public forums
Key requirements- Industry-leading expertise in modern LLMs, multimodal modeling, representation learning, fine-tuning, post-training, benchmarking, and ML systems
- Deep experience in computational biology, AI-for-biology, AI-enabled drug discovery, translational data science, biological foundation models, or scientific discovery platforms
- Ability to scope high-value use cases with scientists, define success criteria, and translate needs into technical execution plans
- Working knowledge of genomic, transcriptomic, perturbation, protein sequence/structure, pathways, imaging, pathology, or time-series biological data
- Strong judgment on mechanism-of-action reasoning, target discovery, perturbation biology, interpretability, and validation
- Experience leading small technical teams through ambiguous problems
- Clear communication with ML researchers, data engineers, scientists, executives, and venture leaders
- Comfort in an entrepreneurial environment aiming to create new capabilities for company creation and scientific discovery
- strong communication
- leadership and team mentorship
- cross-functional collaboration
- LLMs
- multimodal modeling
- representation learning
