Principal / Senior Principal Scientist, Foundation Models For Life Sciences (AI)

TL;DR

Principal / Senior Principal Scientist (AI Life Sciences): Researching and developing large-scale generative models and reasoning frameworks to power automated scientific discovery in medicine with an accent on biological sequences, molecular structures, and multimodal data. Focus on designing end-to-end foundational models, integrating domain-specific constraints, and closing the computational-experimental loop with wet-lab results.

Location: San Francisco, CA, USA

Salary: $288,000 - $480,000 USD

Company

hirify.global is building Scientific Superintelligence to autonomously execute the entire scientific method across medicine, materials, and energy.

What you will do

• Drive research on foundation models for biological sequence design, structure prediction, and multimodal scientific reasoning.
• Design, train, and evaluate large-scale generative models on biological and chemical data.
• Implement the "Lab-in-the-Loop" lifecycle by steering data generation and designing feedback loops where experimental results improve model performance.
• Translate complex biological questions into well-defined ML problems and interpret outputs in collaboration with wet-lab scientists.
• Represent the company's research externally through publications at premier venues and conference presentations.

Requirements

• PhD in Computer Science, Machine Learning, Computational Biology, or a related quantitative field.
• Multiple high-impact first-author publications at premier venues (e.g., NeurIPS, ICML, ICLR, Nature).
• Deep expertise in large-scale generative model architectures and distributed training infrastructure.
• Fluency across ML and at least one life science domain (molecular biology, genomics, protein engineering, etc.).
• Proficiency in ML frameworks such as PyTorch, JAX, or TensorFlow.
• Must be located in San Francisco, CA, USA.

Nice to have

• Experience in computational protein design, specifically antibody and nanobody engineering.
• Experience designing biological sequences or molecular structures with demonstrated wet-lab validation.
• Contributions to open-source ML tools or benchmark datasets for scientific applications.
• Experience with agentic frameworks or active learning loops in scientific contexts.

Culture & Benefits

• Competitive base compensation with bonus potential.
• Generous early-stage equity.
• Comprehensive U.S. and International benefits.
• High-impact environment solving historic challenges with startup speed.