Scientist II / Senior ML Scientist, Data-Efficient Learning For Drug Discovery (AI)

TL;DR

Senior ML Scientist (Drug Discovery): Building models and learning strategies for data-scarce environments with an accent on active learning, meta-learning, and multimodal modeling. Focus on designing data acquisition strategies and creating closed-loop systems to accelerate molecular discovery.

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

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

Company

hirify.global is building Scientific Superintelligence™ to automate the scientific method and accelerate discovery in medicine, materials, and energy.

What you will do

• Build ML models that perform well in low-data regimes for drug discovery and molecular optimization.
• Design data acquisition strategies using active learning, Bayesian optimization, and experimental design to maximize learning.
• Develop multimodal models integrating DEL data, simulation outputs, assay data, and structural information.
• Create closed-loop learning workflows that continuously update models as new experimental and computational data arrives.
• Collaborate with computational chemists and biophysicists to translate model predictions into practical recommendations for compound selection.
• Expose model-driven recommendations as tools for scientists and AI agents.

Requirements

• PhD or equivalent experience in ML, computational chemistry, computational biology, statistics, or computer science.
• Strong experience training ML models in low-data regimes.
• Expertise in active learning, meta-learning, fine-tuning, or uncertainty estimation.
• Experience building ML models for scientific, molecular, or high-dimensional experimental datasets.
• Practical proficiency with PyTorch, JAX, or scikit-learn.
• Must be based in or authorized to work in the USA or UK

Nice to have

• Drug discovery experience, specifically in molecular optimization, screening, or design-make-test-learn workflows.
• General understanding of pharmacology, biochemistry, or mechanisms of molecular activity.
• Experience with closed-loop experimentation, autonomous labs, or agent-driven scientific workflows.
• Familiarity with causal inference, optimal experimental design, or Bayesian methods.

Culture & Benefits

• Competitive base compensation with bonus potential and generous early-stage equity.
• Comprehensive medical, dental, and vision coverage for US employees.
• 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.