Scientist / Senior Scientist, Multimodal & Relational Machine Learning Foundation Models (AI)

Location: San Francisco Bay Area, CA; San Diego, CA

Salary: $179,400–$330,000 annually, depending on level and location.

Company

hirify.global develops cell rejuvenation technologies intended to restore cell health and resilience and address disease, injury, and age-related disabilities.

What you will do

• Design, develop, and evaluate large-scale foundation models for multimodal biological data.
• Pre-train and fine-tune models using natural language, multimodal signals, and structured relational inputs.
• Build hybrid architectures combining large language models with graph neural networks for reasoning over biological knowledge graphs.
• Develop relational foundation models for zero-shot prediction across heterogeneous biological datasets.
• Design efficient data-loading and distributed-training strategies across multiple GPU nodes.
• Transition research prototypes into reliable, scalable production systems and mentor junior staff.

Requirements

• PhD in Computer Science, Machine Learning, or a related quantitative field, plus 5+ years of relevant academic or industry experience.
• Experience developing novel generative AI models, particularly for multimodal integration, GraphRAG, or relational deep learning.
• Deep understanding of Transformers, graph neural networks, diffusion models, and core machine learning principles.
• Very strong Python programming skills and experience with PyTorch, JAX, or Hugging Face Transformers and Accelerate.
• Experience with multi-GPU and distributed training at scale using tools such as DDP, FSDP, DeepSpeed, Megatron, or Ray.
• Peer-reviewed AI/ML research publications at leading conferences such as NeurIPS, ICML, ICLR, or CVPR.

Nice to have

• Experience with tabular foundation models and in-context learning for structured data.
• Experience with native multimodal early-fusion modeling or combining LLMs with knowledge graphs.
• Experience applying machine learning to NGS data, biological imaging, or spatial transcriptomics.
• Experience optimizing large-scale inference through quantization, distillation, or memory-efficient attention.

Culture & Benefits

• Collaborative work across scientific and engineering disciplines.
• Emphasis on scientific excellence, originality, transparency, teamwork, and integrity.
• Opportunities to contribute to seminars, scientific initiatives, and peer-reviewed publications.
• Commitment to belonging, inclusion, and equal employment opportunities.