Generative Model Research Engineer (AI)

TL;DR

Generative Model Research Engineer (AI): Designing and training generative models for molecular data across small molecules, peptides, and mini proteins with an accent on novel architectures, controllable generation, and biological reasoning. Focus on building distributed multi-GPU training pipelines, integrating biological representations, and evaluating whether generated molecules are structurally valid, biologically meaningful, and genuinely novel.

Location: New York headquarters or the Bay Area, United States

Salary: $150,000–$350,000 plus equity

Company

hirify.global is a stealth biotech startup developing biological reasoning and generative models to enable previously inaccessible drug treatments.

What you will do

• Design and build generative architectures for molecular data across multiple modalities.
• Develop training methods that learn from diverse biological signals and generate novel molecular structures.
• Build controllable generation methods for molecules with specified biological properties and chemical constraints.
• Integrate biological representations from the foundation model into generative pipelines.
• Own the research-to-training cycle, including experiment design, distributed training, hyperparameter optimization, and iteration.
• Design evaluation frameworks for biological relevance, structural validity, and molecular novelty.

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 in generative modeling.
• Strong publication record in generative methods at top-tier venues such as NeurIPS, ICML, or ICLR.
• Extensive experience designing, building, and training deep generative models, including novel architectures, training objectives, or sampling methods.
• Proficiency in Python and PyTorch, with experience training models on distributed multi-GPU infrastructure.
• Experience owning the full research-to-training pipeline and shipping production-quality models and code.
• Rigorous experimental practices, including systematic experiment tracking and data-driven evaluation.

Nice to have

• Experience applying generative models to molecular, chemical, or biological data.
• Background in chemistry, biology, computational biology, biophysics, or a related natural science.
• Experience with multimodal learning, cross-modality translation, conditional generation, or controllable generation.
• Contributions to open-source machine learning projects.

Culture & Benefits

• Ownership-driven culture with high standards and a focus on practical results.
• Constructive feedback, transparent communication, and support for professional growth.
• Autonomy over day-to-day work with shared focus on achieving milestones.
• Competitive salary, equity, and strong financial backing.
• Medical, dental, and vision coverage.
• Creative, collaborative, and engaging work environment.