SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: USD 176,000 - 304,000 / annual
Lila Sciences is seeking an ML Scientist to advance RNA and DNA sequence design using machine learning and computational biology. You will develop models and design strategies for understanding and engineering nucleic acid sequences, including 3' UTR optimization, 5' UTR optimization, CDS optimization, and promoter/enhancer design.
You'll work at the intersection of machine learning, sequence modeling, experimental design, and platform development. The role spans both applied design campaigns and building next-generation models that improve how Lila generates, evaluates, and learns from nucleic acid sequence-function data.
Key responsibilities include:
- Building ML models for RNA and DNA sequence design across regulatory and coding sequence contexts
- Developing methods for de novo generation, sequence property prediction, diverse set selection for experimental validation, and active learning strategies
- Investigating biological mechanisms of designed sequences and proposing hypotheses about success or failure
- Partnering with experimental scientists to propose informative assays and validation strategies
- Collaborating with ML scientists and engineers to integrate nucleic acid design models into robust platforms and agent-driven frameworks
- Staying current with research in nucleic acid biology and sharing findings externally through papers or blog posts
Required qualifications:
- PhD or equivalent experience in machine learning, computational biology, bioengineering, computer science, statistics, or related quantitative field
- Hands-on experience building, training, and evaluating ML models for DNA or RNA
- Strong foundation in modern ML methods with practical experience using PyTorch, JAX, or equivalent tools
- Experience developing models for sequence design, sequence-function prediction, generative modeling, or active learning
- Ability to reason about complex biological systems and formulate ML approaches for sequence-function challenges
- Curiosity about nucleic acid biology, including RNA biology and regulatory genomics
- Strong communication and collaboration skills
Bonus experience includes regulatory element design, RNA secondary structure modeling, UTR design, inverse design methods, therapeutic sequence design, wet-lab collaboration, high-throughput experimental datasets, and industry experience translating ML research into practical biological design workflows.
