Senior​/Principal Machine Learning Scientist

Senior / Principal Machine Learning Scientist at Altos Labs

We invite you to apply for the Senior / Principal Machine Learning Scientist role. The ideal candidate will be a hands‑on, creative, and collaborative scientist eager to lead transformative AI research in aging and disease.

Restore cell health and resilience through cell rejuvenation to reverse disease, injury, and age‑related disabilities.

Everyone Owns Achieving Our Inspiring Mission. Diversity, belonging, and inclusion are foundational to our success.

• 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.
• Translate your deep understanding of the mathematical and theoretical underpinnings of cutting‑edge AI research into high‑impact applications.
• 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.
• Act as a key technical mentor to Machine Learning Scientists and Engineers, raising the bar for scientific rigor and model robustness across the organization.

• Proven track record leveraging machine learning to solve real‑world problems.
• 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, Tensor Flow, JAX, or similar.
• Experience with multi‑GPU and distributed training at scale.
• Team player who thrives in collaborative environments and is committed to enabling colleagues to reach their full potential through giving and requesting feedback focused 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.

• Ph.D. in Machine Learning, Computer Science, Artificial Intelligence, Statistics, or a related quantitative field, demonstrating a deep theoretical foundation in ML/AI.
• 6+ years of relevant post‑Ph.D. work experience in either an academic or industry setting.
• Proven experience developing and applying complex machine learning models, preferably with a significant portion of that time spent in a fast‑paced industry or translational research environment.
• A strong track record of leading and publishing innovative, peer‑reviewed research in top‑tier ML conferences (e.g., NeurIPS, ICML, ICLR) or high‑impact scientific journals.
• Excellent scientific communication skills: verbally and in writing; with computational and non‑computational audiences, in informal 1‑to‑1 settings, team meetings, and formal seminars.
• 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,…