Lead deployment of advanced AI/ML solutions (multimodal transformers, graph or sequence models, Bayesian/probabilistic approaches) for toxicity prediction and translational safety applications
Design and implement agentic AI systems tailored to toxicology use cases
Specialize in the fine-tuning and alignment of foundation models for toxicology domain-specific applications and supporting new approach methods (NAMs)
Drive collaboration with cross-functional teams of toxicologists, computational scientists, biologists, and chemists to ensure explainability, reproducibility, and address specific "context of use" regulatory requirements for safety assessments
Champion best practices in model governance, and responsible AI within a regulated environment, helping to establish frameworks for responsible and ethical AI deployment in preclinical research
Present and communicate science in key internal and external toxicology forums.

Ph.D. or M.S. in Computer Science, Computational Biology, Computational Chemistry, Bioinformatics, Statistics, or related field
0+ years post-PhD or 3+ years post-MS experience developing and deploying AI/ML models
Hands-on experience with large language models and agentic AI frameworks (fine-tuning, prompt engineering, multi-agent orchestration, tool use, and API-based production orchestration) required
Proven experience integrating and modeling multimodal datasets (omics, chemical, textual, imaging)
Strong software development skills in Python and familiarity with modern ML frameworks (e.g., PyTorch, TensorFlow), MLOps tools, cloud platforms (AWS preferred), and HPC environments
Excellent communication skills; ability to translate complex technical work to domain experts and leadership.

medical, dental, vision healthcare and other insurance benefits (for employee and family)
retirement benefits, including 401(k)
paid holidays
vacation
compassionate and sick days

Senior Data Science Specialist

Key skills