Principal Scientist — Human Brain Molecular Model

Position details:

Location
:
New Haven, CT - On Site

Type
:
Full Time

Reports to
:
Head of Data Platform

Pay
: $200,000 - $250,000

Our team sits at the intersection of cutting-edge neuroscience, AI, engineering, and translational drug development. This work requires scientific rigor, responsible stewardship, and close collaboration across disciplines.

We are looking for people who are curious, accountable, and ready to tackle unique challenges. At Bexorg, you will work alongside colleagues who ask thoughtful questions, share ideas openly, and take ownership of moving complex work forward.

Whether your background is in neuroscience, biology, engineering, data science, or operations, you can help build a stronger foundation for decisions that shape new CNS medicines.

• You will lead the development of Bexorg's human brain world models: generative models that predict how the molecular state of human brain tissue evolves over time, both in disease and in response to therapeutic intervention.
• You will take ownership of model architecture, training, and validation while helping establish priorities for the experimental data needed to improve model performance. Working closely with our drug discovery and development teams, you will help translate computational predictions into meaningful scientific and therapeutic decisions.
• Your work will focus on Bexorg's proprietary, dynamic human brain molecular omics datasets, modeling molecular trajectories in continuous time across brain regions, cell types, pathways, and genes.
• This is a hands-on scientific leadership role with broad ownership and opportunities for cross-disciplinary collaboration. You will contribute directly to model development while helping shape the experimental strategies that support Bexorg's scientific and drug development goals.
• We are a highly collaborative team with an all-hands approach to advancing the platform. You should be comfortable working across disciplines, learning beyond your original area of expertise, and supporting adjacent teams.

• Lead the architecture, development, training, and validation of generative human brain world models that predict molecular changes over time and in response to interventions.
• Develop models that capture high-dimensional molecular trajectories across brain regions, cell types, pathways, and genes.
• Work with Bexorg's proprietary human brain molecular omics datasets to improve model accuracy, biological relevance, and predictive capabilities.
• Partner with experimental teams to prioritize studies and data generation strategies that strengthen model capabilities and support scientific objectives.
• Apply advanced machine learning methods, including generative modeling, probabilistic learning, and approaches for high-dimensional dynamical systems.
• Incorporate biological inductive biases, uncertainty quantification, and structured inference into model development.
• Translate model predictions through Neurolens into actionable objectives for compound design and prioritization before synthesis.
• Collaborate with drug discovery and development scientists to inform target selection, compound advancement, dosing strategies, and donor-specific experimental contexts.
• Evaluate model capabilities, limitations, and uncertainty to guide scientific decisions and identify opportunities for additional experimentation.
• Develop and maintain reproducible machine learning pipelines with robust data and model lineage.
• Communicate complex computational findings clearly to multidisciplinary teams, supporting practical scientific and drug development decisions.
• Contribute to a collaborative research environment by working across disciplines, sharing expertise, and supporting the continued advancement of Bexorg's platform.

• A PhD in machine learning, computational biology, statistics, physics, or a related quantitative field.
• Postdoctoral or industry research experience.
• Demonstrated experience applying deep learning to solve complex problems in physics or biology.
• Hands-on experience with generative modeling approaches, including diffusion or flow-based methods, and modern probabilistic learning.
• Experience developing models that capture high-dimensional dynamical systems and incorporate symmetries or inductive biases.
• Strong…