Scientist I/ii, Mrna Translation Dynamics

Lila Sciences Lila Sciences · AI Frontier · One Charles Park, Cambridge, MA · Autonomous Science Platform

The Scientist I/II, mRNA Translation Dynamics role at Lila Sciences focuses on developing experimental workflows and generating biological datasets that will be used to train machine learning models. The role involves designing and executing high-throughput screening campaigns, optimizing assays, and collaborating with computational and ML teams to define data requirements and validate model predictions. The goal is to integrate synthetic biology, high-throughput experimentation, and intelligent automation to advance biological discovery.

What you'd actually do

  1. Design and execute high-throughput pooled screening campaigns to interrogate mRNA translation dynamics across diverse sequence and structural contexts
  2. Develop and optimize cell-free and in-cell assay systems for quantitative measurement of translation efficiency, kinetics, and regulation across different cell environments
  3. Collaborate closely with computational and ML teams to define data requirements, validate model predictions, and close the loop between experiment and prediction
  4. Establish and refine next-generation library design strategies, leveraging combinatorial and rational approaches to explore sequence space efficiently
  5. Analyze and interpret complex biological datasets, distilling key findings into actionable insights for platform advancement

Skills

Required

  • 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 have

  • 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

What the JD emphasized

  • generate rich biological datasets that feed directly into Lila's machine learning models
  • define data requirements, validate model predictions, and close the loop between experiment and prediction

Other signals

  • develop workflows that utilize next-generation pooled screening strategies
  • generate rich biological datasets that feed directly into Lila's machine learning models
  • integrating synthetic biology, high-throughput experimentation, and intelligent automation
  • define data requirements, validate model predictions, and close the loop between experiment and prediction
  • contribute to the development of automated and semi-automated experimental pipelines