What You Will Contribute To AltosThe 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.
- Deep hands-on experience designing large-scale perturbation or functional genomics experiments for downstream computational modeling, not as standalone biology.
- Working fluency in ML concepts relevant to biological data, sufficient for substantive technical dialogue with ML leads.
- A clear grasp of how choices in experimental design determine what a model can and cannot learn from the resulting data - and the judgment to design experiments accordingly.
- An out-of-box thinker and a strategic leader in the emerging area of lab-in-the loop for ML workflows and discovery. Be comfortable with exploring areas of robotics and automation in wet-labs that are at the edge of your expertise.
- Sound judgment on build-vs-buy decisions for assays, tools, and models, plus direct experience managing CROs and external vendor relationships.
- Experience building, leading, and growing a technical team, alongside a track record of building or scaling experimental-computational feedback loops.
- Demonstrated ability to lead and influence in a matrix organization - particularly aligning computational and experimental teams that may sit in different reporting lines.
- Director/Sr. Director-level seniority - someone who can both manage people and stay hands-on with experimental design.
Minimum QualificationsEducation: PhD in Machine Learning, Computer Science, Computational Biology, Bioinformatics, or a related quantitative field.
The level will be based on relevant experience and track record of technical and people leadership gained within a biotech or AI driven life sciences environment. Typically:
- Director: PhD + 10+ years experience with a proven track record, with a minimum of 4-5 years of experience, in people management.
- Senior Director: PhD +14+ years experience, with 7-8+ years of people/team leadership, and a demonstrable broad background driving multiple programs through multi disciplinary teams.
The hiring range for Redwood City, CA:
- Director: $265,400 - $340,200
- Senior Director $347,700- $445,800
The hiring range for San Diego, CA:
- Director: $261,500- $335,300
- Senior Director $305,800- $392,100
Exact compensation may vary based on skills, experience, and location.
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