Lila Sciences is solving humankind's greatest challenges by applying AI to every aspect of the scientific method, enabling breakthroughs in human health, climate, and sustainability through scientific superintelligence and autonomous lab technologies.
Requirements
- PhD. or M.S. in Chemistry, Chemical Engineering, Materials Science, or related field.
- â„ 5 years of experience in synthesis and characterization of porous materials (e.g., MOFs, zeolites, oxides etc).
- Strong track record with robotic synthesis platforms, high-throughput experimentation, or automated materials discovery.
- Proficiency in Python for instrument scripting, workflow development, and data handling.
- Deep knowledge of synthetic methodologies and solid-state or solution-phase chemistry.
- Experience with commercial automation platforms (e.g., Chemspeed, Hamilton, Tecan) or custom-built robotic systems.
- Familiarity with in situ or high-throughput characterization methods (e.g., PXRD, adsorption, TGA etc).
Responsibilities
- Design & develop automated synthesis workflows for porous materials across multiple classes and synthetic routes (e.g., solvothermal, hydrothermal, ionothermal).
- Partner with engineering team to build, operate, and optimize robotic platforms and parallel synthesis systems for high-throughput experimentation.
- Partner with engineering team to interface hardware systems (reactors, sensors, analytics) with software pipelines for metadata capture, scheduling, and orchestration.
- Partner with AI teams to enable closed-loop experimentation and iterative hypothesis generation.
- Collaborate across materials science, engineering, and data science teams to drive platform development, documentation, and discovery.
Other
- PhD. or M.S. in Chemistry, Chemical Engineering, Materials Science, or related field.
- â„ 5 years of experience in synthesis and characterization of porous materials.
- Comfort with cloud-based orchestration and scientific software stack integration.
- Contributions to open-source or community-driven lab automation tools.
- Prior work in self-driving labs or AI-guided discovery loops.
