Korro is seeking to drive the development and application of machine learning solutions for genetic medicine discovery and development, applying deep learning and statistical methods across their RNA editing platform.
Requirements
- Deep understanding of modern deep learning theory and practice, including Transformers, sequence models (e.g., state-space models), LLMs, and proven ability to implement, train, and debug high-performance models using PyTorch, JAX, TensorFlow, or R frameworks (torch, tensorflow/keras), with experience in associated libraries such as Flax, Equinox, PyTorch Geometric, or tidymodels.
- Proficiency in scientific computing and data analysis using R (tidyverse, Bioconductor, caret) and/or Python (pandas, numpy, scipy, scikit-learn) ecosystems.
- Experience working with large datasets and understanding the challenges associated with scale, including data preprocessing, feature engineering, distributed training, and cloud platforms (AWS/SageMaker, GCP, Databricks).
- Experience with graph neural networks, molecular representation learning, or willingness to rapidly acquire computational biology expertise.
- Track record of impactful research through publications in high-impact scientific journals with experience leading technical projects and mentoring junior researchers.
- Excellent communication skills, capable of discussing complex ideas with both domain experts and audiences with diverse backgrounds, and experience with ensemble methods, cross-validation, and model evaluation in production environments.
Responsibilities
- Drive research and development of novel deep learning architectures, training paradigms (e.g., supervised, self-supervised, generative, multi-modal), and algorithms tailored for large-scale biological sequence data and related modalities.
- Partner with computational biologists, data scientists, and data engineers to integrate domain expertise, define scientifically meaningful tasks, and apply cutting-edge machine learning research towards ambitious biological challenges.
- Design, implement, and maintain robust ML Operations (MLOps) pipelines for model training, evaluation, versioning, and deployment using cloud-based infrastructure and tools like AWS's MLflow.
- Design and execute statistically rigorous experiments using design of experiments (DOE), A/B testing, and Bayesian approaches to optimize RNA editing strategies, validate model predictions, and advance our mechanistic understanding of RNA editing through testable biological hypotheses.
- Identify and prototype novel machine learning applications across diverse organizational functions including manufacturing optimization, supply chain analytics, regulatory strategy, and clinical trial design.
- Mentor early career scientists and engineers, fostering a culture of technical excellence and scientific curiosity through leadership and code review.
- Contribute to long-term strategic planning for ML/AI platform capabilities, identifying emerging technologies and research directions that could transform genetic medicine development timelines and outcomes.
Other
- PhD (or equivalent expertise) with a strongly distinguished research focus in Machine Learning, Computer Science, Statistics, Physics, or related quantitative field with 4+ years post-graduate experience in leading industrial R&D or highly competitive academic environments.
- Share research findings through internal presentations and contribute to the scientific community via publications or presentations.
- Excellent communication skills, capable of discussing complex ideas with both domain experts and audiences with diverse backgrounds
- competitive compensation, including equity-based compensation, and a comprehensive benefits package that includes medical, dental, vision, 401(k) retirement plan, life insurance, a dependent care flexible spending account and a Company-funded health savings account and free parking.
