Blank Bio

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.

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 machine learning engineer, you will be responsible for scaling our models, building the training infrastructure, and ensuring reproducibility across large-scale biological datasets. You’ll work closely with research scientists and biologists to turn cutting-edge machine learning into practical, high-impact tools for RNA biology. 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 the company.   Responsibilities Develop and optimize large-scale ML training pipelines for RNA foundation models. Implement distributed training systems (multi-GPU/TPU) and optimize performance at scale. Build infrastructure for dataset management, preprocessing, and benchmarking. Collaborate with scientists to translate biological questions into ML tasks. Contribute to the design and evaluation of new architectures, embeddings, and fine-tuning strategies. Maintain high-quality engineering standards, including reproducibility, testing, and deployment readiness. Qualifications Must-haves 3+ years of work experience Proficiency in Python and modern deep learning frameworks (PyTorch, JAX, or TensorFlow). Hands-on experience training large-scale models (transformers, diffusion, or sequence models). Strong background in distributed training, optimization, and performance profiling. Track record of building ML systems that scale and ship. Nice-to-haves Experience with biological or messy, real-world scientific data. Background in computational biology, bioinformatics, or adjacent fields. Experience in early-stage startups or interdisciplinary ML-for-science projects. Compensation & Benefits Competitive salary and meaningful early-stage equity. Comprehensive health, dental, and vision coverage. Generous vacation and parental leave policies.

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 an ML research scientist, you’ll design novel ML methods and develop benchmarks that reflect clinically relevant biology. 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 the company. Responsibilities Design and prototype new ML methods (representation learning, generative modeling, contrastive pretraining) for RNA biology. Develop benchmarks and evaluation frameworks for tasks spanning diagnostics, patient stratification, biomarker discovery, and more Analyze large-scale sequencing datasets (bulk RNA-seq, single-cell, long-read) to inform model development and evaluation. Qualifications Must-haves PhD (or equivalent experience) in Machine Learning, Computational Biology, or related fields. Demonstrated track record in ML research (publications, impactful projects, or deployed systems). Expertise in representation learning, and/or large-scale sequence modeling. Ability to independently design and execute research projects. Nice-to-haves Familiarity with transcriptomics, RNA biology, or other -omics data. Experience developing benchmarks for biological or clinical ML tasks. 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

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 We are looking for a Software Engineer to own the engineering systems that hold our research and product surface together: benchmarking suites, containerization, internal tooling, partner-facing inference, and the reproducibility scaffolding that lets the rest of the team move fast without breaking things. You will work closely with our ML engineers and research scientists. 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 the company. Responsibilities Build and maintain our benchmarking infrastructure for evaluating models against the tasks that matter clinically. Containerize training, evaluation, and inference workloads so they run identically anywhere we ship them, including air-gapped partner environments. Build the data pipelines and internal tooling that scientists use day to day, from raw RNA-seq through preprocessing, QC, and result delivery. Build the inference services partners use to run our models against their own data. Qualifications Must-haves 3+ years of experience shipping production systems Strong proficiency in Python Experience with Docker and container orchestration, including debugging complex or hard-to-reproduce build and deployment issues. Experience owning services end-to-end, from system design and implementation through deployment, monitoring, on-call support, and long-term maintenance. Experience building APIs or tools that make models accessible to other engineers, scientists, or customers Nice-to-haves Familiarity with transcriptomics, RNA biology, or other -omics data. Experience shipping software into regulated, security-sensitive, or air-gapped environments. 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.