Scientist /Senior Scientist, Multimodal & Relational Machine Learning Foundation Models

About the role

As a Staff Machine Learning Scientist, you will focus on designing, developing, and evaluating state-of-the-art foundation models, at scale, to benefit the research. You will also lead the design of efficient data loading strategies and distributed training recipes.

Responsibilities

• Pre-train and fine-tune large-scale machine learning systems using multimodal biological data, natural language, and structured relational inputs.
• Arcitect and implement novel hybrid models that integrate Large Language Models (LLMs) with Graph Neural Networks (GNNs) for multi-hop reasoning over biological knowledge graphs.
• Develop Relational Foundation Models (RFMs) that enable zero-shot predictive tasks over heterogeneous, multi-table biological datasets.
• Lead the design of efficient data loading strategies and distributed training recipes (e.g., FSDP, DeepSpeed) to train models across multiple GPU nodes.
• Gain insights into model performance based on theory, deep research, and the mathematical underpinnings of set-invariant and graph-structured architectures.
• Apply strong coding experience to model development and deployment, ensuring research prototypes transition into reliable, scalable production systems.
• Stay up-to-date on the latest developments in deep learning—including native early-fusion and Mixture-of-Experts (MoE) architectures—and apply this knowledge to Altos' research.
• Mentor junior staff while maintaining a high individual technical contribution to the core research ecosystem and peer-reviewed publications.

Who You Are

• Excited about the Altos mission of restoring cell health and resilience to reverse disease, injury, and age-related disabilities.
• Highly collaborative in mindset and ways of working across research and engineering boundaries.
• Self-motivated to drive and deliver on long-term technical projects and scientific goals.
• Demonstrates the desire to grow professionally and expand their skillset in biology, machine learning, and/or drug development.
• Able to communicate and explain the design, results, and impact of complex AI architectures to both scientific and non-scientific staff.
• Keen to contribute to seminars and scientific initiatives within Altos and the broader AI research community.

Minimum 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).

Preferred Qualifications

• 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.

Benefits

The salary range for Redwood City, CA and San Diego, CA is provided above. Exact compensation may vary based on skills, experience, and location.