Senior Scientist, Machine Learning

The role

You will design, build, and scale state-of-the-art foundation models to tackle biological questions. You will focus on the synthesis of unstructured multimodal signals with structured relational data and knowledge graphs.

What you will do

• Pre-train and fine-tune large-scale machine learning systems using multimodal biological data, natural language, and structured relational inputs.
• Architect and implement novel hybrid models that integrate Large Language Models (LLMs) with Graph Neural Networks (GNNs).
• Develop Relational Foundation Models (RFMs) for zero-shot predictive tasks over heterogeneous biological datasets.
• Lead the design of efficient data loading strategies and distributed training recipes.
• Gain insights into model performance based on theory and mathematical underpinnings.
• Apply coding experience to model development and deployment.
• Stay up-to-date on the latest developments in deep learning.
• Mentor junior staff while maintaining technical contribution and peer-reviewed publications.

Required qualifications

• PhD in Computer Science, Machine Learning, or a similar quantitative field with 5+ years of relevant work experience in academic or industry settings.
• Prior experience in developing and implementing novel generative AI models, specifically in multimodal integration, GraphRAG, or relational deep learning.
• Deep understanding of Machine Learning principles and how they apply to diverse architectures like Transformers, GNNs, and diffusion models.
• Very strong programming skills in Python and deep learning libraries (e.g., PyTorch, JAX, Hugging Face Transformers/Accelerate).
• Proven experience with multi-GPU and distributed training at scale (e.g., DDP, FSDP, DeepSpeed, Megatron, or Ray).
• Strong track record of published, peer-reviewed innovative AI/ML research at top-tier conferences (NeurIPS, ICML, ICLR, CVPR).

Nice to have

• Familiarity with tabular foundation models (e.g., TabPFN) and in-context learning strategies for structured data.
• Specific experience in native multimodal modeling (early-fusion) or the synthesis of LLMs and Knowledge Graphs.
• Track record of ML applied to biological data, such as NGS data (RNA-seq, ATAC-seq), biological imaging (microscopy, IF), or spatial transcriptomics.
• Experience in optimizing large-scale inference via quantization, distillation, or memory-efficient attention mechanisms.

About the company

Altos Labs is a company whose mission is to restore cell health and resilience through cell rejuvenation to reverse disease, injury, and disabilities. Exceptional scientists and industry leaders from around the world work together to advance this shared mission.