Principal Computational Biologist

Are you ready to own the strategy behind how Gordian turns large-scale in vivo perturbation screens into a prioritized, translatable pipeline of therapeutic targets across chronic and age-related disease, and to help build sophisticated analytical frameworks the rest of the team will use to get there?

Large-scale transcriptomic data is at the heart of Gordian's discovery efforts. In this role, you'll set the standard for how our screening and validation decisions are grounded in rigorous, transparent, state-of-the-art analyses, and you'll define the frameworks the rest of the computational team builds on.

The Destination

Gordian Biotechnology is a therapeutics company whose mission is to cure age-related disease and wake up every morning more capable than the day before.

Traditional ex vivo screening methods have failed to produce effective treatments, as age-related diseases have mechanisms driven by complex, multi-cellular interactions with the aged environment. To address this problem, Gordian's Mosaic Screening pools interventions in living animal models of disease, producing datasets that more rapidly enable causal validation for hundreds of targets, in the living context of disease that can be mapped to human patients. This resource lets us make the most informed choices on what new ideas for treating complex disease, and move validated targets into drug development. (more info on our website, and in our preprint).

We are running this discovery engine in successive indication areas, currently focused on cardio-renal-metabolic diseases, to eventually map the effects of every druggable target across every relevant organ in vivo. Our ultimate mission is to develop drugs and run clinical trials, both internally and in collaboration with multiple partners. By pooling the data from each program, we seek to identify medicines with broad impact on the multimorbidity and decline caused by aging.

The Journey

Our mission is audacious, and the path will be full of both challenges and excitement. Two things characterize the Gordian experience: 1) We work as a team, with ownership in our own roles and trust in each other. 2) We strive for extraordinary outcomes, and in doing so grow our skills and capability.

Team - Relying on each other begins with transparency. We set clear goals, visibly connecting individuals and teams to our company objectives. This empowers each of us to make autonomous decisions about our work, knowing how they will affect the bigger picture. Our communication happens out in the open. We give and receive feedback from a perspective of helping each other grow, share mistakes, and ask for help.

Extraordinary - Every day, we ask ourselves, "How could this process or outcome be even better?" Knowing our overall mission, we do what we think will make the most progress, without asking for permission. We don't shy away from big challenges or unknown territory; we find a way to excel.

Gordian is trying to accomplish something tremendously difficult, and that requires people who care deeply about doing exceptional work. We take ownership of our work, ask every day how we can push our science forward faster, and challenge ourselves, and each other, to continually raise the bar. Holding ourselves and each other to that standard has created an environment where each of us grows into a better version of ourselves.

Doing exceptional work also means building a team that can sustain it. We keep standing meetings to a minimum so people can focus on the science, encourage open collaboration through hands-on experimentation and "pre-mortem" discussions that strengthen experimental design, and make time to connect over weekly team lunches. We also encourage people to unplug with an unlimited vacation policy. The combination of mission-focus, high expectations, and enabling people to thrive has created an environment that talent finds rewarding, as evidenced by a voluntary attrition of only ~6%/yr.

If this environment sounds appealing, help us bring it to life. We are at an exciting inflection point: applying our technology to create comprehensive atlases of therapeutic targets across multiple diseases, partnering for financial and intellectual support, and translating these insights into new medicines. We want both your ability and personality along for the ride. Our culture is a source of great pride; it represents both who we are and who we wish to be.

You can dive deeper here:

The Role

Gordian has generated in vivo perturbation data spanning over 1000 targets across Obesity, Heart Failure, Pulmonary Fibrosis, Osteoarthritis, MASH, and Chronic Kidney Disease (CKD), while partnering with external collaborators to build the most comprehensive knowledge graph of disease-relevant, translatable therapeutics in this space. As a Principal Computational Biologist, you go beyond executing individual screen analyses: you pioneer how Gordian strategizes around perturbation data. You help define the methods that get standardized across the computational team, and keep up with the blistering pace of AI in the space, integrating the best tools into our current and future workflows. You partner directly and autonomously with disease-area experts on forward screen planning, with minimal oversight, and you're expected to spot opportunities the rest of the team hasn't yet seen.

Target prioritization and validation are core to this role. You take screen results beyond hit-calling, building the analytical case for which targets are most likely to be both mechanistically valid and therapeutically actionable, and partnering with disease experts to design the follow-on validation that tests those calls. This role carries a strong need for familiarity in the applied ML and perturbation response prediction space given single-cell omics data, spanning perturbation-response models, trajectory inference, and representation learning. You bring real depth here, including applying methods you're already expert into a biological context that's new to you, as well as integrating the new methods being developed daily by the community. You also have experience integrating orthogonal data modalities, such as proteomics, human genetics, biomarkers, and public resources like GTEx or UK Biobank, with our single-cell screen data to strengthen translatability and sharpen target prioritization decisions. Beyond your own projects, you help define and standardize the statistical frameworks, controls, and QC criteria the broader computational team relies on, and you're expected to reason quickly and well about whether a method or dataset from one disease context transfers to another. You'll also help define how Gordian deploys agentic LLM systems to build modular, semi-automated frameworks for QC, analysis, and interpretation across the team, and maintain the standards for inter-team collaborations with our wet-lab functional groups (single cell, molecular biology, and in vivo groups) to ensure conclusions are always met with the most appropriate quantitative analyses, and nuances are always communicated in the most transparent manner. You play a key role in shaping how these experiments are designed.

By three months, you'll be making significant contributions to feature development and screen validation frameworks, evaluating alternative analytical approaches with an emphasis on interpretability tied to mechanism-of-action. At six months, you'll have defined strong positive and negative controls used across multiple screens, be operating independently with disease experts on forward screen planning, and be proposing concrete improvements to team-wide analysis methodology, with visible influence beyond your own individual analyses.

About You

You must have a Ph.D. in Bioinformatics, Computational Biology, Statistics, Computer Science, or a related quantitative field, paired with deep domain expertise in disease biology. You are equally comfortable discussing pathophysiology and experimental design with disease-area and experimental scientists as you are discussing computational models and analytical approaches with computational colleagues.

You have at least 2+ years of hands-on post-graduate industry experience (postdoctoral experience alone does not establish this level), with a demonstrated track record of applying computational biology to single-cell transcriptomic data in preclinical or translational settings. Your experience extends beyond hit-calling or descriptive cell-state characterization to target prioritization, mechanism-of-action investigation, biomarker discovery, or validation, ideally in work that progressed toward or was explicitly designed to support clinical translation.

You are highly capable in R and/or Python and can work fluently with major single-cell ecosystems such as Seurat, Scanpy, and related tools. Importantly, you are able to select and adapt methods based on the biological question rather than being tied to a particular computational framework.

You have experience leveraging large public or external biological resources such as GTEx, UK Biobank and other human genetics/GWAS resources, or other disease-relevant omics datasets, and using them to provide context, validate hypotheses, prioritize targets, identify biomarkers, or inform a biological or translational decision.

You have a genuine drive to develop or substantially extend computational methods when existing approaches are insufficient, with at least one peer-reviewed publication or preprint in which you were a major contributor demonstrating this capability. You can recognize when an analytical problem is genuinely novel, formulate an appropriate computational strategy, and determine how to establish whether the resulting approach is actually useful.

You have a track record of standardizing or scaling analytical workflows across a team, not just for your own projects.

You have demonstrated experience integrating multiple layers of biological evidence to answer a scientific or translational question. This may include single-cell data together with animal phenotypes or assay metadata (e.g. histology, proteomics, etc.). You are comfortable moving from molecular and cellular observations to tissue-level phenotypes and ultimately to hypotheses about mechanisms of action with empirical support.

You bring a strong machine learning and computational modeling background applied in real contexts, deep enough that you can bring rigor to a new biological domain even without prior exposure to it.

You're an excellent interdisciplinary communicator, comfortable being the person a disease lead relies on to make judgment calls independently, and proactive about asking the right clarifying questions when extending your expertise into a new context.

You have experience mentoring junior computational candidates (RA/grauduate level) and working in a group

You have a track record of success in high-agency work, consistently creating momentum rather than waiting for direction.

You want to do your best work alongside exceptional teammates and are energized by environments where people push each other to think more clearly, work at a higher standard, and grow into better versions of themselves.

You truly want to play a key role in an early-stage startup screening new targets for intractable diseases of aging: A fast-paced environment full of both uncertainty and new challenges, demanding relentless resourcefulness.

Additional valuable skills if you have them

Direct prior work in cardio-renal-metabolic biology and relevant tissues (heart, kidney, adipose, liver); if not directly, closely related experience (for example, UK Biobank and other relevant GWAS catalogues, QTL analyses coupled with single-cell data etc.) that demonstrates transferable fluency when it comes to gaining deeper insights into MOA and target prioritization.

Experience with pooled perturbation and CRISPR screening data specifically

Experience with spatial transcriptomics data (10X Xenium, Visium, etc)

Experience with preclinical models for validation with functional readouts (e.g., human explants, organoids, etc.)

The Details:

Gordian aims to provide everything you need to thrive. Beyond our community and science, you'll have enough equity to be a true stakeholder in the company, a competitive salary, full health/dental/vision/life insurance, 401k with match, onsite lunch paid for 3 days a week, an onsite gym, whatever vacation you need to be at your peak, remote work flexibility, and access to world-class mentors and advisors to support your professional growth. Our building is in the heart of the biotech capital of South San Francisco.

The pay range for this role is:

170,000 - 230,000 USD per year (South San Francisco)