AI Research Scientist

About Absentia Labs

Absentia Labs is building mechanistic AI models to predict how drug compounds will behave in the human body before costly preclinical and clinical studies.

Our platform integrates molecular properties, exposure, biological context, experimental evidence, and machine learning to identify potential safety liabilities earlier and explain the biological mechanisms driving them. We initially focus on predictive toxicology and drug safety, with the broader goal of building foundational models that can reason across human biology, pharmacology, and translational outcomes.

We work at the intersection of frontier AI, computational biology, chemistry, toxicology, and drug development.

 

The Role

We are looking for an AI Research Scientist to help advance the modeling approaches at the core of Absentia's platform.

This is a research role for someone who wants to develop new machine learning methods for difficult scientific problems, not simply apply existing models to biological datasets.

You will formulate research questions, design and run experiments, develop novel model architectures and learning strategies, and investigate how models can integrate heterogeneous biological and chemical evidence to predict complex human outcomes.

Our research problems span molecular representation, graph learning, transformers, multimodal learning, representation learning, uncertainty, mechanistic reasoning, and biological generalization.

You will work closely with our CTO, AI/ML engineers, data engineers, and scientists to move promising ideas from research hypotheses into validated modeling capabilities.

The goal is straightforward but difficult: develop AI systems that can reason about how a drug interacts with human biology well enough to make useful predictions before those outcomes are observed experimentally or clinically.

What You’ll Do

• Own ambitious research problems in predictive toxicology, drug safety, and computational biology from hypothesis through experimental validation.

• Develop and evaluate novel deep learning architectures and training methods, including graph neural networks, transformers, multimodal models, generative approaches, and other emerging architectures where scientifically appropriate.

• Investigate representations that connect molecular structure, biological targets, pathways, dose and exposure, pharmacology, toxicology, and clinical outcomes.

• Develop approaches for learning from heterogeneous, sparse, noisy, and partially observed scientific datasets.

• Explore methods for cross-domain and out-of-distribution generalization, including prediction on novel compounds, chemical spaces, biological contexts, and endpoints.