Your Impact at LilaWe are seeking a curious, driven, and collaborative Scientist to join our discovery platform team focused on decoding mRNA translation dynamics. You will develop workflows that utilize next-generation pooled screening strategies like polysome profiling and Ribo-seq to to generate rich biological datasets that feed directly into Lila's machine learning models. This is a unique opportunity to help invent a new approach to biological discovery by integrating synthetic biology, high-throughput experimentation, and intelligent automation.
What You'll Be Building- Design and execute high-throughput pooled screening campaigns to interrogate mRNA translation dynamics across diverse sequence and structural contexts
- Develop and optimize cell-free and in-cell assay systems for quantitative measurement of translation efficiency, kinetics, and regulation across different cell environments
- Collaborate closely with computational and ML teams to define data requirements, validate model predictions, and close the loop between experiment and prediction
- Establish and refine next-generation library design strategies, leveraging combinatorial and rational approaches to explore sequence space efficiently
- Analyze and interpret complex biological datasets, distilling key findings into actionable insights for platform advancement
- Contribute to the development of automated and semi-automated experimental pipelines to increase throughput and consistency
What You'll Need to Succeed- MSc with 4+ years of industry or academic experience, or PhD in a relevant field (molecular biology, bioengineering, synthetic biology, chemical biology, etc.)
- Deep expertise in mRNA biology and translation regulation
- Experience with next-generation sequencing based assays of mRNA translation such as polysome profiling and Ribo-seq
- Strong quantitative and analytical skills with experience handling large-scale biological datasets
- Excellent communication and collaboration skills with a track record of working effectively in interdisciplinary teams
Bonus Points For- Experience integrating experimental data with machine learning or computational modeling pipelines
- Proficiency in Python, R, or other scripting languages for data analysis and visualization
- Background in generating and screening complex, high-diversity sequence libraries
- Familiarity with laboratory automation, liquid handling systems, or high-throughput workflow development
- Experience with in-situ sequencing of RNA with imaging like STARmap and Ribomap
CompensationWe offer competitive compensation including bonus potential and generous early equity. The final offer will reflect your unique background, expertise, and impact.
Expected Base Salary Range
$108,000-$170,000 USD
