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About AbbVie
AbbVie is a global biopharmaceutical company focused on discovering and delivering innovative medicines and solutions in immunology, oncology, neuroscience, and eye care. Its products include Humira, Skyrizi, and Rinvoq.
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As a Senior Scientist I/II, Pharmaceutical Development – Thermodynamics & AI/ML Applications (all genders), you will join a multidisciplinary team to design and apply thermodynamics-based and AI/ML-enabled solutions for pharmaceutical formulation and process development. You will be responsible for translating complex physicochemical and process data into predictive insights regarding solubility, phase behavior, physical stability, and material interactions. Your daily work involves building hybrid modeling approaches that combine first-principles understanding with machine learning methods to create in-silico digital tools and decision-support workflows. To succeed, you must apply thermodynamic and quantum mechanical principles while utilizing AI/ML for scientific problems. You will manage model validation, uncertainty assessment, and data interpretation to guide formulation selection and risk assessments. This role solves critical challenges in drug substance performance by integrating mechanistic insights with experimental data to support robust product design.
Develop and apply predictive models to guide formulation and process development decisions across pharmaceutical programs.
Apply thermodynamic and quantum mechanical principles to predict material behaviors like solubility, phase behavior, and crystallization.
Build and improve models for physical stability, material compatibility, and excipient interactions of drug products.
Create hybrid tools that integrate mechanistic physics-based insights with machine learning and historical data.
Execute robust model validation strategies including uncertainty assessment and applicability domain evaluation.
Convert complex physicochemical datasets into actionable insights for risk assessments and experiment prioritization.
Communicate technical findings and model limitations clearly to both technical and non-technical stakeholders.
What we're looking for
PhD in pharmaceutical sciences, chemical engineering, physical chemistry, materials science, data science for physical systems, or related disciplines.
Strong understanding of thermodynamics and its application to pharmaceutical materials, formulations, and process performance.
Proven ability to perform quantitative modeling and analyze/interpret physicochemical experimental data.
Experience using AI/ML for scientific or engineering problems with a focus on data quality, model suitability, and interpretability.
Ability to combine mechanistic insight with data-driven methods to address real-world development challenges.
Proficiency in structuring analyses, documenting methods, and presenting conclusions in a business-relevant manner.
Strong collaboration and professional communication skills for working across cross-functional, technical, and international teams.
Excellent written and verbal communication skills in English.
CHO Cell Culture
Bioreactor Perfusion
Process Analytical Technology (PAT)
Design of Experiments (DOE)
Cell Metabolic Modeling
Machine Learning
AMBR
cGMP
Protein Characterization
scale up
scale down
Shake Flasks
Python
R
Machine Learning
Data Science
High-Content Imaging
Cell Culture
Flow Cytometry
ELISA
Genomics
Cloud Computing
Large Language Models
Generative AI
Analytical Pipelines
Statistics
In Vitro Assays
Organ-on-Chip
Remote (Foster City, CA)21 days ago
$169,320–$219,120
Python
PyTorch
JAX
Machine Learning
Deep Learning
Generative Modeling
Protein Language Models
Foundation Models
Geometric Neural Networks
AlphaFold
Active Learning
Multimodal Learning
Representation Learning
Data Integration
Design-Build-Test-Learn