Principal ML Scientist – Predictive Toxicology

What we expect from you

• Strong deep learning foundations for molecular AI, for example experience with the architectures commonly used for molecular property modelling (e.g. graph neural networks, message-passing and transformer-based models).
• A profile that clearly demonstrates you understand the concerns that drive toxicity assessment in drug discovery — whatever the specific toxicity endpoints you've worked on (for example DILI, cytotoxicity, or micronucleus/genotoxicity imaging readouts).
• Tangible experience building predictive models and driving the adoption of toxicity models in real drug-discovery programmes or industrial R&D pipelines, working closely with teams to get models into pipelines.
• Working knowledge of how RNA-seq, toxicity screens and image-based screens are used in pharma as part of routine HTS and compound triage.
• Scientific leadership excellence: able to set vision, own a scientific agenda, and lead technical and customer conversations independently.
• Comfortable staying hands-on in the modelling while setting scientific direction and mentoring others — this is a scientific leadership role first, with the opportunity to build and lead a team over time.
• PhD or equivalent experience in a relevant field (computational biology, cheminformatics, toxicology, ML, or similar), plus 6+ years applying ML to drug discovery/life science problems.

Nice to have

• Experience with federated learning, privacy-preserving ML, or other multi-party training environments.
• Evidence of prospectively validating predictive toxicity models and using them to influence compound design, prioritisation or progression decisions in live drug-discovery programmes.
• Production-grade model delivery in regulated, enterprise, pharmaceutical, or biotech settings, and/or a publication record in relevant computational biology, toxicology, or ML venues.
• Multi-omics and high-content imaging experience (e.g. cell painting).
• Familiarity with public toxicology and bioactivity data resources (e.g. Tox21, ToxCast, LINCS/L1000) and mechanistic frameworks such as adverse outcome pathways.

What we offer you

• Industry-competitive compensation, including early-stage virtual share options
• Remote-first working – work where you work best
• Wellbeing budget, mental health support, work-from-home budget, co-working stipend, and learning budget
• Generous holiday allowance
• Office Days at our Berlin HQ or a different European location (3x per year)
• A high-calibre, execution-focused team with experience from leading organizations