Scientist I / II, mRNA Translation Dynamics

Lila Sciences•Cambridge, MA

8d•$108,000 - $170,000

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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