Protein Ligand Docking Data Jobs

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AI Engineer III – Computational Drug Discovery AI Model Development

Sam Quest Solutions Inc. Remote 🌍 Remote

Remote

Today

$70 - $100/hr Full time Ai Model Training Artificial Intelligence Computational Drug Discovery

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Job Description

Sam Quest Solutions Inc. is hiring an AI Engineer III to help build and refine AI/ML modeling capabilities for computational drug discovery. This role centers on developing, fine-tuning, and benchmarking models that move from protein/ligand sequence inputs to predictions of 3D structure and affinity, supporting multiple lead optimization programs.

In this remote position, you will design automated workflows and evaluate model performance across scientific datasets, with a focus on runtime efficiency and reproducible results. The work includes comparing alternative pose generation and docking approaches and determining how models should be structured for project-specific versus cross-project use.

What you’ll do

Fine-tune pre-trained foundation models such as Boltz-2 and AISB using domain-specific project data
Compile and curate datasets from multiple LO projects, including experimental protein-ligand complex structures and potency or affinity measurements
Analyze dataset variation to stress-test model applicability domains
Benchmark model performance against established methods including DeepAutoQSAR, GatorAffinity, MPNN, MM-GBSA, FEP, and related approaches
Compare co-folder pose generation against docking-based methods
Measure recall and precision using project-specific thresholds and build model ranking based on confidence and applicability domain
Assess whether a single fine-tuned co-folder model can serve multiple projects or whether project-specific models are required
Design an automated, end-to-end workflow optimized to produce single predictions in under a minute using GPU or CPU

What you bring

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 skills, including scripting and automation
Ability to build reproducible, scalable model workflows with runtime efficiency in mind
Collaboration and communication skills across scientific and technical teams

Technologies

Python
GPU, CPU
Boltz-2, AISB
DeepAutoQSAR, GatorAffinity
MPNN, MM-GBSA, FEP

Compensation and location

Location: Remote
Pay: USD 70 - 100 per hour

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