Senior Computational Biologist

About the Company

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 complex causes that include interactions with the aged environment. To address this problem, Gordian’s Mosaic Screening pools interventions in living animals, producing datasets with causal validation for hundreds of targets, in the living context of disease and mapped to human patients. This data lets us make the most informed choices on what new ideas for treating complex disease, and move validated targets into drug development.

We are running this discovery engine in successive indication areas, currently focused on cardiometabolic, to map the in vivo effects of every druggable target across every relevant organ. We then develop drugs and run clinical trials, both internally and in collaboration with multiple partners. By pooling the data from each program, we can start to identify medicines with broad impact on the multimorbidity and decline caused by aging.

The Gordian experience is characterized by teamwork with deep ownership and trust, and a drive for extraordinary outcomes that force us to grow our capability. We set clear goals, empower autonomous decisions, and maintain open communication where we share mistakes, ask for help, and give feedback to help each other grow. We take ownership of our work, challenge each other to raise the bar, and foster an environment where everyone can thrive. We minimize standing meetings, encourage open collaboration, and offer an unlimited vacation policy to support work-life balance.

About the Role

Gordian is at an exciting inflection point, having recently generated in vivo perturbation data spanning over 500 targets across different in vivo contexts (Obesity, Heart Failure), and are actively expanding this to Chronic Kidney Disease (CKD). This growing resource is being utilized in collaboration with external partners to build the most comprehensive knowledge-graph of disease-relevant, translatable therapeutics spanning these disease areas.

As a Computational Biologist at Gordian, your mission is to leverage our in vivo screens to decode how cells respond to genetic perturbation at the transcriptomic level, and translate those responses into predictions of physiologically-relevant, therapeutically-actionable outcomes in disease. You’ll focus on cardio-renal-metabolic indications and associated tissues (heart, kidney, adipose, liver, etc.), partnering closely with our disease-area experts and experimental teams to translate screen results into clear, testable biological hypotheses.

• Guide key analytical decisions across the screen lifecycle — from experimental design and power calculations, to QC thresholds and dataset integration strategies, to the statistical frameworks used for hit calling and prioritization for validation.
• Develop and apply robust methods for modeling heterogeneous biological contexts (e.g., cell-type-specific perturbation responses, animal, batch or treatment context variability), identifying and correcting for confounders (e.g., cell cycle, ambient RNA, doublets, batch effects), and selecting or designing appropriate positive and negative controls to validate effect sizes and method performance.
• Communicate findings with rigorous attention to interpretability and generalizability, ensuring QC metrics, model outputs, and troubleshooting insights flow back to the single-cell and experimental teams to iteratively improve assay design and data generation.
• Help define how we deploy agentic LLM systems to build modular, semi-automated frameworks for in-house QC, analysis, and interpretation — integrating cutting-edge computational methods (e.g., perturbation-response models, trajectory inference, representation learning) with clinically relevant genomic resources.
• Collaborate with disease experts and the in vivo team to connect perturbation-driven molecular changes to in vivo physiology, identifying high-confidence features that capture desirable phenotypes, and build a prioritized set of candidate targets for future screens.
• Establish and iterate on selection criteria for validation that improves screening efficiency and translatability across programs.

In your first month, you’ll become fluent in our in-house pipelines and workflows and independently propose analysis tasks supporting our Obesity and Heart Failure programs. By three months, you’ll make significant contributions to feature development and/or validation, including evaluating alternative analytical approaches. At six months, you’ll help define strong positive and negative controls for these screens, partner independently with disease experts on forward screen planning, and use existing validation comparisons to assess predictive power, proposing concrete improvements to analysis methodologies.

Requirements

• PhD in Bioinformatics, Computational Biology, or a quantitative field, paired with deep domain expertise in disease biology, bridging pathophysiology and computational model architecture.
• 2+ years of post-graduate experience (industry or postdoc) analyzing single-cell transcriptomic data across diverse biological contexts.
• Proven productivity with at least one peer-reviewed publication or preprint demonstrating a major co-author contribution to a computational method or adapted analysis framework applied to a disease-relevant system.
• Experience building or adapting novel analysis methods, integrating foundation models, perturbation prediction, and trajectory modeling to map cell-state transitions.
• Strong statistical foundations regarding controls, confounders, and interpretability in single-cell data.
• Proficiency in Python and R (e.g., Scanpy, Seurat), with the ability to move fluidly between ecosystems and familiarity with NGS workflows (FASTQ, BAM) to reason about data quality.
• Experience scaling impact by standardizing recurring analyses into repeatable workflows and using agentic LLM systems to semi-automate them.
• Excellent interdisciplinary collaboration skills, self-motivation, and comfort with ambiguity, particularly in close partnership with experimental teams to ground work in real biological questions around in vivo perturbation studies.
• Ability to communicate results through clear visualizations and concise summaries that resonate with both computational and experimental audiences.

Valuable additional skills include:

• Experience with pooled perturbation and screening data (e.g., CRISPR or barcode-driven screens) and single-cell perturbation analysis methods.
• Prior work in cardio-renal-metabolic biology and relevant tissues (heart, kidney, adipose, liver).
• Experience integrating public genomics resources for large-scale workflows (HPC or cloud).
• Experience with preclinical validation models like human explants or organoids.

Benefits

• Competitive salary and equity.
• Comprehensive health, dental, vision, and life insurance.
• 401k match.
• Paid onsite lunch 3 days a week.
• Onsite gym.
• Unlimited vacation.
• Access to world-class mentors.

Our office is located in South San Francisco.