Team Lead, Chemistry; Peptides Lausanne
Listed on 2025-10-20
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Research/Development
Drug Discovery, Research Scientist
Team Lead, Chemistry (Peptides), London, Lausanne
Isomorphic Labs is applying frontier AI to help unlock deeper scientific insights, faster breakthroughs, and life‑changing medicines with an ambition to solve all disease.
About IsoIsomorphic Labs (Iso Labs) was launched in 2021 to advance human health by building on and beyond the Nobel‑winning Alpha Fold 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.
About The Exploratory Sciences TeamJoin our expanding Exploratory Sciences team, focused on tackling proteins which are critical for disease biology, but have been traditionally considered intractable. We are a small, multi‑disciplinary team within the Drug Design and Medical Research function at Isomorphic Labs. We work at the interface of machine learning, chemical biology and drug discovery to apply our platform to some of the toughest problems in drug discovery, and we collaborate closely with multiple teams across Isomorphic Labs.
We have a strong growth mindset and prioritise continued development and learning as we hope to build the next generation of drug discovery scientists.
As a member of the Peptides team, you will deliver the development and application of a state‑of‑the‑art peptide design platform. This will involve applying our platform in novel ways, and critically driving portfolio projects towards the clinic. You will need to design and execute on bold strategies to tackle problems that are currently considered impossible. Working alongside world‑leading AI/ML and Drug Design Platform teams, you will deliver scientific expertise and strategic direction to drive both improvements in the Platform and help to solve unsolved drug design problems.
WhatYou Will Do
- Apply your state‑of‑the‑art knowledge of peptide design and synthesis to compose novel approaches to long‑standing challenges in drug discovery.
- Utilise internal AI/ML models to solve challenging drug discovery problems.
- Collaborate in multidisciplinary teams to support data‑driven delivery of high quality scientific solutions. This will require understanding the fundamentals of diverse disciplines including ML, Biology, Chemistry and DMPK, and communicating effectively with experts in those fields.
- Manage members of the growing team and CRO resource, mentoring and supporting their development.
- Represent the company externally, interacting with existing and prospective collaborators and contract research organisations (CROs).
- Prepare detailed scientific proposals and reports to support new and ongoing programs.
- Embrace and champion our culture of inclusion and continuous professional development.
- PhD in Chemistry, or a related technical field, or equivalent industrial experience.
- 5+ years’ professional experience in a Chemistry role, either operating within a biotech or pharma setting, or within a Post‑Doc position.
- Practical experience in the design and synthesis of therapeutic peptides, ideally applying these skills to active drug discovery projects.
- Experience of leading a high‑performing team, mentoring and empowering people to thrive in their roles.
- Experience of coding in Python, or a willingness to learn in‑role.
- A genuine passion and enthusiasm for applying AI/ML to drug design and how this can transform the field.
- Excellent communication (written and verbal) and interpersonal skills, with an ability to work effectively in a cross‑functional, collaborative team environment.
- A track record of creative problem solving with a strong growth mindset, adaptability, and eagerness to learn new concepts and techniques.
- Experience working with or managing activities at contract research organisations (CROs).
- Experience with peptide screening techniques.
- Experience of contributing to structure‑based drug design (SBDD) problems.
- Experience of applying ML methods to drug discovery problems.
- Track record of authorship of relevant scientific manuscripts.
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