> Markdown version of [/jobs/ext/1982459-principal-ml-scientist-predictive-toxicology](https://www.wearedevelopers.com/jobs/ext/1982459-principal-ml-scientist-predictive-toxicology). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal ML Scientist - Predictive Toxicology - **Company:** Jobgether - **Location:** Germany (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Artificial Neural Networks, Computational Biology, Machine Learning, Deep Learning, Data Strategy - **Published:** August 8, 2026 - **Apply:** https://www.adzuna.de/details/5832195369 ## About the Role The ideal candidate combines deep expertise in machine learning applied to life sciences with strong scientific leadership and the ability to work independently across technical and customer-facing environments. * PhD or equivalent experience in computational biology, cheminformatics, toxicology, machine learning, or a related scientific discipline. * 6+ years of experience applying machine learning techniques to drug discovery, computational biology, or life science challenges. * Strong understanding of deep learning methods for molecular AI and predictive modelling. * Proven experience developing predictive toxicity models and supporting their adoption within pharmaceutical or industrial research environments. * Knowledge of toxicity assessment workflows, including areas such as DILI, cytotoxicity, genotoxicity, or related safety endpoints. * Experience working with biological datasets such as RNA-seq, toxicity screening data, image-based screening, or high-throughput screening workflows. * Ability to define scientific vision, lead technical discussions, and communicate effectively with customers, partners, and internal teams. * Strong hands-on modelling skills combined with the ability to guide scientific strategy and mentor others. * Excellent analytical, problem-solving, and communication skills. * Professional working proficiency in English., * Experience with federated learning, privacy-preserving machine learning, or distributed AI systems. * Experience validating predictive toxicity models prospectively and influencing compound design or prioritization decisions. * Experience deploying production-grade ML solutions in regulated, enterprise, pharmaceutical, or biotech environments. * Publication record in computational biology, toxicology, or machine learning research. * Knowledge of multi-omics, high-content imaging, cell painting, or mechanistic biological frameworks. * Familiarity with public toxicology and bioactivity datasets such as Tox21, ToxCast, or LINCS/L1000. ## Description This role offers the opportunity to shape the future of machine learning applications in life sciences and drug discovery. You will lead the scientific strategy behind predictive toxicology and quantitative biology initiatives, transforming complex biological data into impactful AI-driven solutions. Working at the intersection of machine learning, computational biology, and pharmaceutical research, you will help develop models that improve therapeutic discovery and decision-making. The position combines scientific leadership with hands-on technical contribution, allowing you to influence both product direction and customer outcomes. You will collaborate with industry partners, researchers, and technical teams to integrate advanced modelling approaches into real-world workflows. This is a high-impact opportunity for a scientist who wants autonomy, ownership, and the chance to advance AI-powered innovation in healthcare. Accountabilities: The Principal ML Scientist will own the expansion of predictive toxicology and quantitative biology capabilities, defining scientific direction and delivering machine learning solutions that create value for life sciences partners. * Lead the development and execution of the scientific strategy for predictive toxicology, quantitative biology, and related drug discovery workflows. * Define modelling approaches, biological endpoints, and data strategies that support better safety and efficacy decisions in pharmaceutical research. * Build and optimize machine learning models using advanced molecular AI techniques, including approaches such as graph neural networks, message-passing architectures, and transformer-based models. * Apply federated learning approaches to enable collaborative model development across multiple organizations while maintaining data privacy and ownership. * Integrate scientific workflows involving areas such as multi-omics, image-based screening, high-throughput screening, and compound prioritization into scalable solutions. * Collaborate directly with customers and scientific partners, leading discussions around evaluation, adoption, delivery, and roadmap development. * Translate complex scientific challenges into practical AI solutions that can be incorporated into real drug discovery programs. * Mentor other scientists and contribute to building future scientific capabilities within the organization. ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Hacking Your Vacation: Using Data for Fun](https://www.wearedevelopers.com/videos/585-hacking-your-vacation-using-data-for-fun) - [What non-automotive Machine Learning projects can learn from automotive Machine Learning projects](https://www.wearedevelopers.com/videos/397-what-non-automotive-machine-learning-projects-can-learn-from-automotive-machine-learning-projects) - [Big Business, Big Barriers? Stress-Testing AI Initiatives.](https://www.wearedevelopers.com/videos/1022-big-business-big-barriers-stress-testing-ai-initiatives) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [The Biggest German Tech Companies](https://www.wearedevelopers.com/magazine/424-the-biggest-german-tech-companies) - [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market) - [The Fastest-Growing Tech Sectors to Look Out for in 2025](https://www.wearedevelopers.com/magazine/373-the-fastest-growing-tech-sectors-to-look-out-for-in-2025) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Find a Developer Job: 12 Best Job Sites For Developers](https://www.wearedevelopers.com/magazine/165-find-a-developer-job-12-best-job-sites-for-developers)