Computational Biologist

About Blank Bio Blank Bio is an applied AI research lab focused on increasing the success rates of clinical trials. We do this by training RNA foundation models that learn the patterns that shape disease progression and patient response to treatment. We aim to help pharma make more informed decisions in clinical trials by capturing the biology that makes each patient’s tumour unique. We’re a technical team of AI scientists and engineers from companies including Recursion, Deep Genomics, DeepMind, and Amazon, and institutions including Memorial Sloan Kettering Cancer Centre, Stanford, and the Vector Institute. The Role As a Computational Biologist, you’ll help lead the biological interpretation and clinical translation of our RNA foundation models. You'll take embeddings, prognostic risk scores, and predictive signals out of our foundation models and turn them into biomarker case studies that pharma and diagnostic teams can act on. As an early-stage startup, we move fast, work across disciplines, and embrace ambiguity. We’re looking for people who thrive in dynamic environments, are eager to take ownership, and want to help define both the science and the culture of an early-stage startup. Responsibilities Apply Blank Bio’s foundation models and embeddings to real-world clinical datasets to identify predictive and prognostic biomarker case studies in oncology. Analyze literature, clinical trial readouts, regulatory submissions, and biomarker strategies to identify precedents that inform modeling priorities and business development strategy. Curate biomarker-relevant evaluation tasks and datasets that reflect clinically meaningful biology and feed directly into the research team’s benchmark development. Qualifications Must-haves PhD (or equivalent experience) in computational biology, RNA biology, cancer biology, genomics, bioinformatics, or a related quantitative biomedical field Ability to connect model outputs (i.e., embeddings), statistical results, and biological context into clear scientific conclusions Strong written communication skills, with the ability to write for both technical and non-technical audiences Experience performing large-scale omics analyses across public or clinical datasets, such as TCGA, GTEx, ICGC, GEO, dbGaP, or similar resources. Nice-to-haves Familiarity with clinical trial design, including endpoints, patient stratification, all-comer studies, and biomarker-guided trials. Familiarity with ML model evaluation, embeddings, representation learning, or benchmark design in biology. Prior collaboration with clinical researchers, diagnostic developers, or biomarker discovery teams. Previous work in an early-stage, fast-paced environment. Compensation & Benefits Competitive salary and meaningful early-stage equity. Comprehensive health, dental, and vision coverage. Generous vacation and parental leave policies. #J-18808-Ljbffr Blank Bio