Our Mission

Our mission is to restore cell health and resilience through cell rejuvenation to reverse disease, injury, and the disabilities that can occur throughout life.

For more information, see our website at altoslabs.com.

Our Value

Our Single Altos Value: Everyone Owns Achieving Our Inspiring Mission.

Diversity at Altos

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. Our intentional focus is on Belonging, so that all employees know that they are valued for their unique perspectives. We are all accountable for sustaining a diverse and inclusive environment.

What You Will Contribute To Altos

The rate limit on ML for biology is rarely the model. It is the loop between what the lab measures and what the model needs. Altos is looking for a Director or Senior Director-level scientist to own that loop across in vitro and in vivo systems: closed-loop systems where predictions steer the next experiment and results retrain the model, and rigorous standalone verification of model outputs where the wet-lab design still has to hold up. As IoC's ML-for-biology programs scale, the loop between experimental design and model development becomes an increasingly critical driver of progress — the tighter that loop, the faster and more reliably we can improve both. We're looking for a leader who will push the envelope of the lab-in-the-loop with deep expertise in wet-lab biology and machine learning.

Responsibilities:

Lead a team responsible for closed-loop experimental systems that generate data purpose-built to train and validate large-scale foundation and hybrid models for biology.
Lead experimental verification of model outputs that fall outside the tight iterative loop — for example, validating target hypotheses from the hybrid modeling program — ensuring these results are rigorous enough to inform go/no-go decisions even when they don't directly retrain a model.
Partner directly with ML leads across multimodal predictive modeling, foundation model pretraining, and hybrid/mechanistic modeling efforts to ensure model outputs — including in silico perturbation predictions and target hypotheses — are experimentally testable, and that experimental results feed back efficiently to improve model performance.
Architect and implement innovative experimental designs that target a ML-first data generation strategy: what to measure, at what scale and modality, to most efficiently reduce model uncertainty, verify and improve identifiability of hybrid/mechanistic models.
Look strategically beyond current needs to identify and develop new in vitro and in vivo model systems required to meet the longer-term needs of IoC's models — treating the lab not just as infrastructure to operate, but as a lever for generating the most scientifically valuable data possible.
Determine which tools, assays, and models should be developed in-house versus sourced externally; manage relationships with CROs and external vendors where outsourcing is the right call; Provide strategic guidance to the Institute Director on experimental budget planning, balancing long-term innovation with immediate project requirements.
Set standards for experimental design, metadata capture, and automation so lab-generated data is maximally useful with ML in the loop, and mentor talent in this direction.
Act as the technical bridge between experimental and computational teams across IoC and partner Institutes.
Lead effectively in a matrixed environment, coordinating experimental priorities across IoC and partner Institutes where you don't hold direct authority over all contributing teams; Mentor the experimental team to work efficiently with the computational partners in the institute, fostering seamless cross-functional collaboration.

Who You Are

PhD (or equivalent) in a relevant field - genomics, systems biology, bioengineering, or a related discipline - with 10+ years of post-PhD experience, including senior leadership roles.