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

Responsibilities 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) 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.

Salary Range Redwood City, CA

Scientist I, Machine Learning

$200,900 - $257,500 Scientist II, Machine Learning

$226,200 - $290,000 Senior Scientist I, Machine Learning

$257,400 - $330,000 San Diego, CA

Scientist I, Machine Learning

$179,400 - $230,000 Scientist II, Machine Learning

$212,900 - $273,000 Senior Scientist I, Machine Learning

$239,500 - $307,000 Equal Opportunity Employment Altos Labs provides equal employment opportunities to all employees and applicants for employment, without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

Altos prohibits unlawful discrimination and harassment.

This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training. #J-18808-Ljbffr