[Hiring] Principal ML Scientist – Predictive Toxicology @Apheris

Role Description

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:

• Set scientific vision.
• Lead customer and consortium conversations.
• Integrate real scientific workflows into our platform, turning ambitious scientific goals into models that get used in real drug programmes.
• Operate with a high degree of autonomy, owning this agenda end-to-end and acting as a scientific counterpart to customers and partners.

What you will do

• Own our expansion into predictive toxicology and quantitative biology.
• Take the lead as we grow beyond ADME into the science shaping safe, efficacious therapeutics (e.g., 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.
• Decide how best to use relevant data.
• 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.
• Integrate these workflows into our platform so customers can run them at scale.
• Build models that matter by applying federated learning across partner data to deliver models with performance and applicability no single organisation could achieve.
• Work closely with industrial partners to embed them in real drug-discovery pipelines.
• 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.

Qualifications

• Strong deep learning foundations for molecular AI, with experience in architectures commonly used for molecular property modelling (e.g., graph neural networks, message-passing, and transformer-based models).
• A profile that demonstrates understanding of the concerns driving toxicity assessment in drug discovery (e.g., 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 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 modelling while setting scientific direction and mentoring others.
• 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.

Benefits

• 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.