Altos Labs is hiring a Senior Machine Learning Scientist to accelerate the development of unified, multi-modal generative foundation models for multiscale biology. In this role, you will partner with multidisciplinary Scientists and Engineers to design, build, and scale state-of-the-art models that tackle biological questions and aid in the discovery of novel interventions for aging and disease.
What You'll 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) 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.
What We're Looking For
- 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.
Technical Stack
- Languages & Frameworks: Python, PyTorch, JAX, Hugging Face Transformers/Accelerate
- Distributed Training: DDP, FSDP, DeepSpeed, Megatron, Ray
- Model Architectures: Large Language Models (LLMs), Graph Neural Networks (GNNs), Diffusion models, Transformer architectures
Team & Environment
You will work within multidisciplinary teams in the Institute of Computation at Altos Labs.
Benefits & Compensation
- Compensation range: $270,600 - $330,000 in Redwood City, CA; $251,700 - $307,000 in San Diego, CA.
Work Mode
This is a local, onsite role in either Redwood City, CA or San Diego, CA.
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.
