Altos Labs aims to restore cell health and resilience through cell rejuvenation to reverse disease, injury, and disabilities. This role focuses on developing generative AI/ML models for multi-modal, multiscale biology to transform how aging and disease are treated.
Requirements
- Expertise in one or more of the following: generative models, language models, computer vision, bayesian inference, causal reasoning & inference, transfer & multi-task learning, diffusion models, graph neural networks, active learning, cooperative agents
- Experience writing production-quality code with modern machine learning frameworks such as PyTorch, TensorFlow, JAX, or similar
- Experience with multi-GPU and distributed training at scale
- Expertise in several of the following: deep learning, reinforcement learning, generative models, language models, computer vision, Bayesian inference, causal reasoning & inference, transfer & multi-task learning, graph neural networks, active learning, hybrid mechanistic/ML models
- Proven experience applying sophisticated ML techniques to molecular and cell biological data sets (e.g., NGS, spatial omics, bioimaging).
Responsibilities
- Pioneer novel machine learning methodologies and statistical frameworks (e.g., generative models, causal inference, diffusion models, and advanced transformer architectures) to address fundamental challenges in cell health and rejuvenation
- Contribute to setting the long-term technical vision and research strategy for a core domain (e.g., multi-modal data fusion, perturbation modeling) within the Institute of Computation
- Design, implement, and optimize large-scale machine learning systems using modern frameworks (e.g., PyTorch, JAX) and agile practices
- Develop and manage efficient distributed training strategies across multiple GPUs and compute clusters to handle terabytes of multi-modal biological data
- Develop robust approaches for multi-modal data integration and cross-domain mapping to extract actionable biological insights
- Apply computational thinking to solve problems in drug target identification, compound assessment, and prediction of cellular perturbation responses
- Lead the full ML development lifecycle from theoretical conception and data strategy through model development, training, and evaluation
Other
- Act as a key technical mentor to Machine Learning Scientists and Engineers, raising the bar for scientific rigor and model robustness across the organization.
- A team player who thrives in collaborative environments and is committed to enabling colleagues to reach their full potential through giving and requesting feedback focussed on professional growth
- Able to advise others across the wider function / company on cutting edge practices and approaches to enable the science / research.
- Desire to constantly expand your skillset and knowledge. Keen to learn more about biology, computational science, and medicine;
- Inspired by the Altos mission of restoring cell health and resilience to reverse disease, injury, and age-related disabilities.
