Director / Senior Director, Machine Learning for Biology

What You Will Contribute To Altos

Altos is asking questions biology has no method for yet; the Institute of Computing (IoC) builds the machine learning to answer them, and it is looking for a Director or Senior Director-level Scientist to lead that capability with a proven record of taking large-scale ML methods to real biological problems. Someone who is still deep enough in the work to be hands-on in the technical trenches and shape method choices, and senior enough to set strategic direction, at critical decision points, across the institute's most ambitious programs.

• Partner with the rest of the IoC Leadership Team to co-define and steer Altos’ core technical agenda, strategically identifying and prioritizing the most critical biological questions where machine learning can drive breakthroughs in cell rejuvenation and resilience, ensuring ML investments are deployed against highest-impact scientific frontiers rather than pure technical advancement.
• Build and maintain strong partnerships with key stakeholders and leaders across different functions / Altos.
• Lead a team of ML Scientists and Engineers, while staying hands-on enough to provide direct technical guidance across IoC's highest-impact ML programs.
• Provide technical and strategic guidance and advice on ML project design and de-risking across multiple teams, drawing on deep intuition for what drives success or failure in ML-for-biology projects, backed by established experience.
• Provide sustained technical mentorship and strategic guidance, supporting the professional development and growth of the team while proactively growing IoC's ML bench through training, coaching, and active recruitment of outstanding talent.
• Lead effectively in a matrixed environment, influencing technical direction on projects and teams outside your direct reporting line, and aligning priorities across IoC's ML programs even where you don't hold reporting authority.
• Deep knowledge and understanding with common and state of the art AI/ML methods and innovations and large-scale training procedures, applied to biological data modalities (e.g., single-cell, perturbation-response, spatial, or sequence data).
• We are looking for a broad expertise and mastery across various AI/ML domains, specifically: reasoning and open-ended learning framework design for scientific hypothesis generation and evaluation; large-scale pretraining, fine-tuning, and post-training of foundation models, including RL, distillations, finetuning, and strategies to reduce hallucination and align model objectives with scientific discovery; model architectures that span multimodal generative and predictive modeling (normalizing flows, diffusion models, LLMs) for out-of-distribution generalization in biological systems; and hybrid modeling approaches — model identifiability, robustness, and regularized/interpretable methods such as anchor or energy-based models — for biological tasks, including target identification; semantic-based dynamic inference, virtual cell and world model paradigms.
• A strong track record of leading innovative biological ML projects to success, while foreseeing and mitigating ML project failure modes at scale.
• Ability to think strategically, and can optimize for the long term while acting and delivering with pace.
• Ability to contribute to a culture of continuous learning and technical innovation, ensuring a rigorous balance between creative exploration and strategic resource allocation and priorities.
• Stay current with scientific and data science advancements, integrating relevant knowledge into computational biology.
• Intellectually curious with a strong pulse on the computational Landscape, and ability to decipher and prioritize AI trends from hype to maintain a competitive edge.

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