Scientist I/II, mRNA Translation Dynamics

Your Impact at LILA

We 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