Blank Bio
Machine Learning Research Scientist
New
150000 –250000 USD / yearUSASeniormachine learningrna sequencinggenerative modelingrepresentation learningcontrastive pretraining
Design novel machine learning methods and develop benchmarks to improve clinical trial success rates through RNA biology.
Responsibilities
- 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.
- 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.
Requirements
- 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 have
- 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.
Conditions
- Competitive salary and meaningful early-stage equity.
- Comprehensive health, dental, and vision coverage.
- Generous vacation and parental leave policies
Other
- 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.