Head of Toxicology

About the Company Deep Origin is building an operating system for science that transforms how life science research is conducted. Led by Michael Antonov, co-founder of Oculus, and backed by Formic Ventures, we are redefining the infrastructure behind modern drug discovery.

We are now building the next-generation platform for predicting drug toxicity in silico, transforming how pharmaceutical companies evaluate safety before clinical trials. Our mission is to reduce failure rates, accelerate drug development, and eliminate unnecessary animal testing through high-fidelity computational models of human biology.

We are not building incremental QSAR tools. We are building foundational infrastructure for predictive toxicology in the age of AI, systems biology, and large-scale computation.

About the Role We are seeking a Head of Toxicology to own, define, and scale our computational toxicology platform end-to-end. This role operates as the chief subject matter expert, technical visionary, and regulatory expert of our tox platform.

You will work cross-functionally with ML/AI teams, computational biologists, toxicologists, engineers, and commercial teams to build a category-defining platform for predictive toxicology.

This role requires both deep technical leadership and executive-level strategic thinking, with the ability to translate cutting-edge science into scalable, enterprise-ready systems.

Requirements Technical Depth in Computational Toxicology

• 15+ years in computational biology, toxicology, drug discovery, or related domain.
• Proven experience designing and deploying computational models for toxicity or biological systems.
• Demonstrated ability to develop novel modeling approaches beyond industry-standard methods.
• Deep hands-on experience designing and deploying computational toxicology models across mechanistic, statistical, and machine learning paradigms.
• Demonstrated ability to design innovative modeling approaches, not just apply existing frameworks.
• Expertise in integrating ML/AI, systems biology, PK/PD modeling, and real-world data into cohesive predictive systems.
• Strong track record evaluating tradeoffs between mechanistic modeling and statistical/ML approaches.
• Ability to identify breakthrough opportunities beyond current industry standards.
• Experience developing novel model architectures, not just QSAR or legacy approaches.
• Comfortable discussing mechanistic toxicity pathways, ML architectures, regulatory strategy, and platform design.

Deep expertise in one or more of:

• Predictive/computational toxicology.
• Systems pharmacology or systems biology.
• Mechanistic modeling.
• ML/AI in drug discovery.
• PK/PD or ADMET modeling.

Domain & Industry Knowledge.

• Strong understanding of preclinical safety workflows.
• Familiarity with regulatory frameworks (FDA, EMA, ICH).
• Experience working with complex and proprietary pharma datasets.
• Awareness of data limitations, bias, and validation challenges.

Executive Leadership

• Ability to operate as both a strategic leader and a technical decision-maker.
• Executive presence with senior pharma stakeholders and FDA.
• Strong communication and cross-functional leadership skills.

Builder & Entrepreneurial Mindset

• Experience building platforms, teams, or systems from zero to scale.
• Comfortable operating in ambiguity and defining direction.
• Strong bias toward action, ownership, and iteration.

Key ResponsibilitiesScientific & Technical Vision

• Define and drive the long-term roadmap for in silico toxicity prediction.
• Design innovative modeling approaches across mechanistic and ML paradigms.
• Integrate ML/AI, systems biology, PK/PD modeling, and real-world data into a unified platform.
• Evaluate tradeoffs between mechanistic modeling and statistical learning approaches.
• Identify breakthrough opportunities beyond current industry standards.

Platform & Product Leadership