Research Scientist I/II, Computational Organic Electronics
Lila Sciences · Cambridge, MA USA · Alewife, Cambridge, MA
CHECKED 14H AGO
Bio & Science AIonsitemid-level$176k–$304k
At a glance
Highlights
- Use AI and physics-based simulation to discover organic electronic materials.
- Model charge transport and excited-state behavior for device performance.
- Close the loop between computation and experiment to guide optimization.
What they offer
- Bonus potential
- Generous early-stage equity
- Medical, dental and vision coverage
- Employer-paid life and disability insurance
- Flexible time off with company-wide holidays
- Paid parental leave
- Educational assistance program
- Commuter benefits including bike share
- Company subsidized lunch program
Ideal candidate
PhD-trained materials researcher who loves organic electronics and connecting physics insight to real devices. Hands-on, curious collaborator eager to turn simulations and data into actionable discovery with AI and experimental teams.
Requirements
- PhD or equivalent experience in Materials Science, Chemistry, Chemical Engineering, Mechanical Engineering, Physics or related field
- Strong foundation in computational materials science and chemistry including electronic structure methods and large-scale atomistic simulations
- Deep understanding of organic semiconductors, organic electronics, photovoltaics, optoelectronic materials, charge transport or related systems
- Experience applying first-principles, molecular simulations or general atomistic methods to materials discovery or optimization
- Ability to connect molecular, morphological and electronic structure features to device-relevant properties
- Strong programming skills in Python and scientific computing workflows
Lab profile
About Lila Sciences
Building an autonomous science platform where AI, automation, and biology co-evolve for medicine and materials.
What they build
- Autonomous Science Platform
- AI-designed biologics validated in wet-lab
- Electro-optic materials and photonic devices
Known for
- Large-scale automated data generation
- High-throughput protein purification and analytics
- Protein design and BioML modeling
- Robotic orchestration and lab simulation