Senior ML Scientist (Cofolding And Structure-Aware ML)

TL;DR

Senior ML Scientist (Cofolding and Structure-Aware ML): Training next-generation cofolding models for drug discovery with an accent on protein-ligand interactions, representation learning, and experimental data integration. Focus on developing structure-aware ML methods and building rigorous evaluation frameworks to drive AI-driven discovery decisions.

Location: Cambridge, MA USA; London, UK; San Francisco, CA USA

Salary: $228,000 - $358,000 USD (US-based positions)

Company

hirify.global is building Scientific Superintelligence to accelerate discovery across medicine, materials, and energy using an autonomous operating system for science.

What you will do

• Train and evaluate cofolding models for protein-ligand and related molecular discovery applications.
• Implement contrastive, self-supervised, and multimodal learning to improve model performance.
• Develop modeling approaches to extract binding, enrichment, and selectivity signals from DEL datasets.
• Build structure-aware ML models using equivariant GNNs and AlphaFold-style cofolding methods.
• Design rigorous evaluation frameworks to distinguish meaningful molecular learning from dataset artifacts or leakage.
• Collaborate with chemists and biophysicists to connect model outputs to physically and chemically meaningful hypotheses.

Requirements

• PhD or equivalent experience in Machine Learning, Computational Biology, Chemistry, Bioinformatics, or Computer Science.
• Hands-on experience training deep learning models for molecular, protein, or structural biology applications.
• Expertise in contrastive learning, representation learning, or self-supervised learning.
• Experience with protein-ligand modeling, cofolding, or geometric deep learning.
• Proficiency with PyTorch, JAX, or equivalent ML frameworks.
• Strong understanding of data quality, negative construction, and benchmark design for scientific ML.

Nice to have

• Direct experience working with DEL data.
• Experience with Boltz, AlphaFold, diffusion models, or protein language models.
• Knowledge of distributed model training and large-scale scientific data pipelines.
• Familiarity with active learning or integrating ML models into agentic scientific workflows.

Culture & Benefits

• Competitive base compensation, bonus potential, and generous early-stage equity.
• Comprehensive US benefits including medical, dental, vision, and employer-paid life/disability insurance.
• Flexible time off with generous company-wide holidays and paid parental leave.
• Educational assistance program and commuter benefits for office-based employees.
• Company-subsidized lunch program.
• Regionalized benefit programs for full-time employees based outside the U.S.