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About the Role
Contribute to accelerating and optimizing the development of unified, multi-modal generative foundation models for multiscale biology. Be an integral part of multidisciplinary teams building computational platforms to enable Altos to achieve its mission. Partner and collaborate with Scientists and Engineers to design, build, and scale foundation models that tackle biological questions and aid in the discovery of novel interventions for aging and disease. Focus on the synthesis of unstructured multimodal signals with structured relational data and knowledge graphs.
Responsibilities
- Design, develop, and evaluate state-of-the-art foundation models at scale.
- 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 integrating Large Language Models (LLMs) with Graph Neural Networks (GNNs) for multi-hop reasoning over biological knowledge graphs.
- Develop Relational Foundation Models (RFMs) for 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) for multi-GPU node training.
- Gain insights into model performance based on theory, research, and 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 deep learning developments (e.g., native early-fusion, Mixture-of-Experts) and apply knowledge to research.
- Mentor junior staff while maintaining high individual technical contribution and contributing to peer-reviewed publications.
Requirements
- Excited about the Altos mission of restoring cell health and resilience.
- 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 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.
- PhD in Computer Science, Machine Learning, or a similar quantitative field.
- 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 their application 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).
- 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.
Skills
- Generative AI models
- Multimodal integration
- GraphRAG
- Relational deep learning
- Machine Learning principles
- Transformers
- GNNs
- Diffusion models
- Python
- PyTorch
- JAX
- Hugging Face Transformers/Accelerate
- Multi-GPU training
- Distributed training
- FSDP
- DeepSpeed
- DDP
- Megatron
- Ray
- Tabular foundation models
- In-context learning
- LLMs
- Knowledge Graphs
- NGS data
- Biological imaging
- Spatial transcriptomics
- Inference optimization
- Quantization
- Distillation
- Memory-efficient attention mechanisms
Location
- Redwood City, CA
- San Diego, CA
Work Type
- Full-time
Experience Level
- 5+ years of relevant work experience
- PhD
Education Level
- PhD in Computer Science, Machine Learning, or a similar quantitative field
Salary/Compensations
- Redwood City, CA: Scientist I, Machine Learning: $200,900 - $257,500
- Redwood City, CA: Scientist II, Machine Learning: $226,200 - $290,000
- Redwood City, CA: Senior Scientist I, Machine Learning: $257,400 - $330,000
- San Diego, CA: Scientist I, Machine Learning: $179,400 - $230,000
- San Diego, CA: Scientist II, Machine Learning: $212,900 - $273,000
- San Diego, CA: Senior Scientist I, Machine Learning: $239,500 - $307,000
About the Company
- Altos Labs' mission is to restore cell health and resilience through cell rejuvenation to reverse disease, injury, and the disabilities that can occur throughout life.
- Altos Labs has been named one of the Top 3 Biotech Companies and ranked for the second year on the Forbes 2026 Best Startups in America list.
- At Altos, exceptional scientists and industry leaders from around the world work together to advance a shared mission.
- Altos Labs strives for a culture of scientific excellence, learning, and belonging.
Equal Opportunity
- 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.