AI Engineer III – Computational Drug Discovery AI Model Development

About this role

We're looking for an AI Engineer III to support the development, fine-tuning, and benchmarking of AI/ML models for computational drug discovery — building a scalable workflow that goes from protein and ligand sequence straight to 3D structure and affinity prediction with minimal manual intervention, across multiple lead optimization programs.

What You'll Be Doing:
Fine-tune pre-trained foundation models (e.g. Boltz-2, AISB) on domain-specific project data
Compile datasets from multiple LO projects, spanning experimental protein-ligand complex structures and potency/affinity data
Evaluate dataset variation to challenge model applicability domains
Benchmark model performance against established methods (DeepAutoQSAR, GatorAffinity, MPNN, MM-GBSA, FEP, etc.)
Compare co-folder pose generation vs. docking-based methods
Evaluate recall and precision using project-specific thresholds; build model ranking based on confidence and applicability domain
Determine whether one fine-tuned co-folder model can serve multiple projects or whether project-specific models are needed
Design an automated, end-to-end workflow optimized to generate single predictions in under a minute on GPU or CPU

What We're Looking For:
Strong experience in machine learning, computational chemistry, cheminformatics, or related scientific data science domains
Experience training and fine-tuning models on domain-specific scientific datasets
Experience with 3D modeling workflows, pose generation, or docking-based comparison studies
Working knowledge of benchmarking methodologies and model performance evaluation
Strong Python and scripting/automation skills
Ability to design reproducible, scalable model workflows with runtime efficiency in mind
Strong collaboration and communication skills across scientific and technical teams

Join us if you’re passionate about harnessing the power of artificial intelligence to accelerate drug discovery! Bring your expertise in mac...