Head of Bio AI - Radial

Key Outcomes (12–24 Months)

Technical Architecture & Program Leadership

• Establish AI and computational architecture across current and future programs, beginning with DiffUSE and its expansion across multiple structural biology data modalities.
• Shape which foundational bottlenecks Radial addresses and how they become durable technical systems.
• Help make build-vs-fund-vs-partner decisions across Radial’s program portfolio.

Hands-On Building

• Conceive, architect, and directly prototype early modeling systems and core infrastructure.
• Design scalable training workflows, data pipelines, validation frameworks, and evaluation benchmarks.
• Evolve capabilities from early prototyping to production-grade, open platforms the scientific community can build on.

Team & Culture

• Build a high-caliber AI and engineering team, starting small and scaling deliberately.
• Set the technical bar through hands-on contribution and architectural authorship.
• Establish a culture of technical rigor, intellectual honesty, and disciplined experimentation.

Ecosystem & Impact

• Engage leading scientific collaborators and build technically rigorous partnerships.
• Represent Radial’s technical vision within the AI and life sciences communities.
• Ensure outputs are structured and shared as durable public goods.

Competencies

Functional Expertise

• Deep expertise in modern ML: large-scale training, representation learning, generative modeling (diffusion, transformers, foundation models).
• Track record of translating ideas into working, scalable systems.
• Understanding of the interplay between machine learning and physics-based modeling.
• Direct experience formulating and solving inverse problems, including familiarity with ill-posedness, regularization strategies, and the trade-offs between learned and model-based reconstruction approaches.
• Experience building and iterating on ML pipelines for large-scale, data-intensive problems, including efficient data ingestion, preprocessing of high-dimensional inputs, and training workflows that scale across distributed compute resources.
• Systems-level thinking across data generation, modeling, infrastructure, and experimentation.
• Experience designing technical platforms intended to endure and compound over time.
• Scientific range beyond a single modality can engage across problem domains.

Leadership Attributes

• Has managed small technical teams; knows how to attract and retain exceptional talent.
• Hands-on builder who sets the standard through their own work.
• Deep familiarity with the frontier AI x Bio landscape.
• Operates effectively in ambiguity; creates clarity through disciplined technical judgment.

Cultural Alignment

• Motivated by the chance to build something that doesn’t exist yet.
• Committed to open science and durable public infrastructure.
• Thrives in early-stage, open-ended environments blending AI and biology.