Senior Scientist, Multimodal & Relational ML for Biology

Altos Labs; San Diego (CA); On-site; USD 179,000 - 307,000; Full time

Job summary

Altos Labs seeks a Staff Scientist in Machine Learning to design, train, and evaluate state-of-the-art foundation models at scale for multiscale biology. You will collaborate across the Institute of Computation to build platforms enabling discovery of novel interventions for aging and disease.

You will mentor junior staff while contributing to peer-reviewed research, staying current with MoE and early-fusion architectures, and translating research into reliable production systems.

Qualifications

• PhD in Computer Science, Machine Learning, or a similar quantitative field with 5+ years of experience.
• Experience developing generative AI models, multimodal integration, GraphRAG, or relational DL.
• Strong knowledge of Transformers, GNNs, and diffusion models.
• Proficiency in Python and DL libraries such as PyTorch, JAX, Hugging Face.

Responsibilities

• Pre-train and fine-tune large-scale ML systems on multimodal biological data and structured inputs.
• Architect hybrid models integrating LLMs with GNNs for multi-hop reasoning on biological graphs.
• Develop Relational Foundation Models for zero-shot tasks across heterogeneous data.
• Lead distributed training strategies across multiple GPU nodes and optimize data loading.
• Evaluate model performance using theoretical and empirical analysis; contribute to publications.
• Mentor junior staff and ensure prototypes scale to production environments.

Skills

Python

PyTorch

JAX

Transformers

DeepSpeed

Multi-GPU training

Graph Neural Networks

Knowledge graphs

Education

PhD in Computer Science or related field

Tools

Megatron

Ray

Hugging Face Accelerate

Job description

Altos Labs seeks a Staff Scientist in Machine Learning to design, train, and evaluate state-of-the-art foundation models at scale for multiscale biology. You will collaborate across the Institute of Computation to build platforms enabling discovery of novel interventions for aging and disease.

You will mentor junior staff while contributing to peer-reviewed research, staying current with MoE and early-fusion architectures, and translating research into reliable production systems.