Principal ML Scientist – Predictive Toxicology

🔥 11 minutes ago

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Apheris

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2019

🏥 Healthcare

💼 Consulting

📦 Logistics

Healthcare • Consulting • Logistics

Apheris is a company that specializes in enabling secure and compliant data collaboration across distributed data environments, particularly for enterprises. The company's solutions empower organizations to engage in federated machine learning and analytics, facilitating the building of models without needing to move sensitive data, thus preserving privacy and security. Apheris focuses on providing technology that allows multi-party data ecosystems and partnerships, with a strong emphasis on compliance, particularly in regulated industries like pharmaceuticals and biotech. Their technology is trusted by major pharmaceutical companies to support AI-driven drug discovery initiatives without compromising proprietary data.

📋 Description

• Own the company’s expansion into predictive toxicology and quantitative biology • Set the scientific strategy for in silico toxicology and quantitative biology workflows across federated networks • Define valuable endpoints, assays, and modelling approaches for real drug-discovery decisions • Determine how relevant data and techniques can deliver scientific and commercial value • Apply federated learning across partner data to build high-performing models • Integrate scientific workflows into the platform so customers can run them at scale • Work with industrial partners to embed models in drug-discovery pipelines • Lead customer and partner scientific conversations, owning scope, evaluation, delivery, and adoption • Shape the product roadmap around scientific and commercial needs • Remain hands-on in modelling while setting scientific direction and mentoring others

🎯 Requirements

• Strong deep learning foundations for molecular AI • Experience with graph neural networks, message-passing, and transformer-based models for molecular property modelling • Understanding of toxicity assessment concerns in drug discovery, including endpoints such as DILI, cytotoxicity, or micronucleus/genotoxicity imaging readouts • Experience building predictive models and driving adoption of toxicity models in real drug-discovery programmes or industrial R&D pipelines • Working knowledge of RNA-seq, toxicity screens, and image-based screens in pharma HTS and compound triage • Ability to set scientific vision, own a scientific agenda, and lead technical and customer conversations independently • Ability to remain hands-on in modelling while setting scientific direction and mentoring others • PhD or equivalent experience in computational biology, cheminformatics, toxicology, ML, or a similar relevant field • 6+ years applying ML to drug-discovery or life-science problems • Nice to have: experience with federated learning, privacy-preserving ML, or multi-party training environments • Nice to have: evidence of prospective validation and use of predictive toxicity models in live drug-discovery programmes • Nice to have: production-grade model delivery in regulated, enterprise, pharmaceutical, or biotech settings and/or relevant publication record • Nice to have: multi-omics and high-content imaging experience, such as cell painting • Nice to have: familiarity with Tox21, ToxCast, LINCS/L1000, and 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 • 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