Principal ML Scientist – Predictive Toxicology

We're looking for an experienced principal scientist to own and grow our expansion into small molecule predictive toxicology and quantitative biology. This is a hands-on scientific leadership role: you'll set scientific vision, lead customer and consortium conversations, and integrate real scientific workflows into our platform, turning ambitious scientific goals into models that get used in real drug programmes.

What you'll do

• Own our expansion into predictive toxicology and quantitative biology, leading growth beyond ADME into the science shaping safe, efficacious therapeutics (for example, multi-omics technologies, image-based screening, high-throughput screening and compound-triage cascades).
• Set the scientific strategy: define how in silico toxicology and quantitative biology workflows come together across our networks, and which endpoints, assays and modelling approaches deliver value in real drug-discovery decisions.
• Bring your understanding of how these techniques and data are generated and embedded in pharmaceutical R&D, and turn it into a clear view of how to extract the most scientific and commercial value from them.
• Span multiple scientific surfaces: bring depth across the readouts and endpoints that matter for safety and efficacy, from structure-based off-target liability through to pathway-level, mechanistic interpretation and in vivo pharmacokinetics.
• Build models that matter by applying federated learning across partner data to deliver models with performance and applicability no single organisation could achieve.
• Lead the scientific conversation with customers and partners, owning scope, evaluation, delivery and adoption in live drug programmes, while shaping the roadmap around genuine scientific and commercial need.

What you'll need

• 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.
• 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).

What they offer

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