Research Scientist (AI/Biotech)

TL;DR

Research Scientist (AI/Biotech): Advancing a biological reasoning foundation model by designing architectures, training objectives, and evaluation systems with an accent on representation learning, multimodal biological data, and large-scale pretraining. Focus on designing unified molecular representations, training models on distributed multi-GPU infrastructure, and measuring genuine biological reasoning across molecular interactions, properties, and function.

Location: New York HQ or Bay Area, United States

Salary: $150,000–$350,000 plus equity

Company

hirify.global is a stealth-stage AI and biotechnology startup developing foundation models for biological reasoning and drug discovery.

What you will do

• Advance foundation-model architecture and training objectives designed for biological reasoning.
• Develop unified representations across molecular and other heterogeneous biological data modalities.
• Extend model reasoning across molecular binding, properties, biological function, and related phenomena.
• Own pretraining end to end, including experiment design, distributed multi-GPU training, hyperparameter optimization, and iteration.
• Design evaluation frameworks that distinguish genuine biological reasoning from statistical memorization.

Requirements

• PhD in computer science, machine learning, physics, mathematics, or a related field with 2+ years of postdoctoral or industry research experience; alternatively, a bachelor's or master's degree with 5+ years of hands-on research and engineering experience.
• Strong publication record at top-tier venues, with contributions to pretraining, self-supervised learning, representation learning, or foundation models.
• Hands-on experience pretraining large models on diverse, heterogeneous data and scaling training infrastructure.
• Proficiency in Python and PyTorch, including distributed training on multi-GPU infrastructure.
• Experience owning the full research-to-training pipeline and shipping production-quality, well-tested code.
• Strong experimental design, systematic experiment tracking, and data-driven analysis skills.

Nice to have

• Background in chemistry, biology, computational biology, biophysics, or a related natural science.
• Experience pretraining models on molecular or biological data.
• Experience with multimodal learning or heterogeneous data sources.
• Contributions to open-source machine learning projects.

Culture & Benefits

• Ownership-oriented culture with autonomy over day-to-day work and accountability for milestones.
• High standards, constructive feedback, and support for professional growth.
• Encouragement of creativity, contrarian thinking, and practical, results-oriented ideas.
• Competitive compensation with equity in a well-funded startup.
• Medical, dental, and vision coverage.