Toxicology Lead, London, Lausanne

About Isomorphic Labs

Isomorphic Labs (IsoLabs) was launched in 2021 to advance human health by building on and beyond the Nobel-winning AlphaFold system. Since then, our interdisciplinary team of drug discovery experts and machine learning specialists has built powerful new predictive and generative AI models that accelerate scientific discovery at digital speed. Our name comes from the belief that there is an underlying symmetry between biology and information science. By harnessing AI’s powerful capabilities, we can model complex biological phenomena to design novel molecules, anticipate drug performance, and develop innovative medicines for some of the world’s most devastating diseases. We have built a world‑leading drug design engine and continually innovate on model architecture and capabilities to advance rational drug design. Every day, we get closer to the promise of digital biology and our ambitious mission to solve all disease with the help of AI.

The Preclinical Development team accelerates delivery of high‑quality candidates by developing innovative, AI‑first solutions. Collaboration with drug discovery disciplines and AI/ML researchers ensures safety, druggability, and developability are central to our drug design engine. We shape the development of state‑of‑the‑art toxicology predictive models and rethink drug discovery from HitID to First in Human trials by optimizing model context.

Toxicology Lead – Your Impact

As a Toxicology Lead, you will develop safety assessment strategies for drug discovery and development projects from inception to regulatory submission. Working closely with the drug design platforms (small molecules and biologics), you will drive the optimisation of Iso’s assets with an AI‑first approach. You will guide development of our AI predictive toxicology platform to increase success probability and contribute to reducing animal testing in collaboration with AI/ML research and data strategy teams.

What You Will Do

• Design and implement safety assessment strategies with an AI‑first approach for small molecules and biologics drug discovery projects.
• Design screening cascades and mechanistic experiments to support project progression in a data‑driven manner.
• Develop SAR understanding of toxicology main liabilities to optimise chemical and biological matter where needed.
• Define regulatory safety assessment packages to support successful acceptance by regulatory agencies.
• Author toxicology sections of regulatory documents.

Predictive toxicology strategy

• Partner with Machine Learning colleagues to build the predictive toxicology aspects of the drug design platform and identify, generate and analyse data to develop and improve predictive toxicology models.
• Identify state‑of‑the‑art technologies and methods that could bring transformational benefits to our platform or ways of working.

CRO interactions

• Maintain and develop close relationships with the CRO network to ensure high quality and timely data generation.
• Leverage external scientific expertise to enhance the team's impact.
• Oversee delivery of data generation campaigns purposefully designed to support AI‑based toxicology models.

People

• Mentor junior members of the team.
• Embrace and champion a culture of inclusion and continuous professional development.

Skills and Qualifications

Essential

• Ph.D. in Toxicology, Pharmacology, Biology, or DVM/MD with relevant experience.
• Board certification.
• 7–10+ years of professional experience in a Toxicology role within biotech or pharma.
• Demonstrated experience filing successful INDs/CTAs for small molecules and biologics.
• Deep understanding of global regulatory guidelines for safety assessment.
• Experience partnering closely with Discovery and Clinical teams.
• Excellent written and verbal communication and interpersonal skills.
• Track record of creative problem solving with a growth mindset, adaptability, and eagerness to learn new concepts.

Nice to have

• Experience with peptides and other new modalities.
• Good understanding of machine learning techniques.
• Familiarity with Python and KNIME.

Culture and values

• Thoughtful – curiosity, creativity, and care.
• Brave – fearlessness, initiative, and integrity.
• Determined – confidence in hypotheses and urgency.
• Together – collaboration and catalytic relationships.

Hybrid working

We follow a hybrid model and require you to come into the office 3 days a week (currently Tuesday, Wednesday, and one other day depending on your team). If you have additional needs that prevent you from following this hybrid approach, we’d be happy to discuss accommodations if you’re selected for an initial screening call.

Equal Employment Opportunity Statement

We are committed to equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy or related condition (including breastfeeding) or any other basis protected by applicable law. If you have a disability or additional need that requires accommodation, please let us know.