Senior Scientist, Machine Learning

Company Summary: Korro is a biopharmaceutical company focused on developing a new class of genetic medicines for both rare and highly prevalent diseases using its proprietary RNA editing platform. Korro is generating a portfolio of differentiated programs that are designed to harness the body’s natural RNA editing process to effect a precise yet transient single base edit. By editing RNA instead of DNA, Korro is expanding the reach of genetic medicines by delivering additional precision and tunability, which has the potential for increased specificity and improved long-term tolerability. Using an oligonucleotide-based approach, Korro expects to bring its medicines to patients by leveraging its proprietary platform with precedented delivery modalities, manufacturing know-how, and established regulatory pathways of approved oligonucleotide drugs. Korro’s lead program is Alpha-1 Antitrypsin Deficiency (AATD). Korro is based in Cambridge, Massachusetts.
We are collaborative and united by a common mission. We are building a company with extraordinary people with an audacious vision to create transformative genetic medicines for prevalent diseases. Our values - Rewrite the future, On the Cutting Edge, Better Together, Dynamically Different, Kindness and Integrity form the fabric of the organization. They are reinforced daily and serve as key dimensions in the hiring process to help us ensure that Korro is a magnet for outstanding talent and a great place to work. Join us as we redefine what's possible in genetic medicine and work to make a lasting impact on human health.
Position Summary:We are seeking an experienced and strategic Senior Scientist, Machine Learning to drive the development and application of machine learning solutions for genetic medicine discovery and development. This role offers the opportunity to apply deep learning and statistical methods in current and novel areas across our RNA editing platform, from target identification and oligonucleotide design to patient stratification and clinical optimization. The ideal candidate will bring deep expertise in both machine learning and statistics, enabling rigorous experimental design, hypothesis testing, and model validation in complex biological systems. You will have significant autonomy to identify and explore new applications of ML/AI across diverse functions within the organization, working at the intersection of computational biology, drug discovery, and precision medicine.

Key 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.
• Share research findings through internal presentations and contribute to the scientific community via publications or presentations.

Required Qualifications:

• 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.
• 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/k