Research Machine Learning Federated Learning Jobs in Missouri

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.

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.

Nice-to-have qualifications:

• 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.

• Competitive compensation package, including virtual share options.
• Fully remote-first working model with flexibility to work from the location that suits you best.
• Wellbeing budget and mental health support.
• Work-from-home budget and co-working stipend.
• Learning and professional development budget.
• Generous holiday allowance.
• Opportunities to participate in office days at European locations several times per year.
• Collaboration with a highly skilled, international team with experience from leading organizations.