Computational Organic Electronics Scientist (AI/ML)

Socket.dev; Cambridge (MA); On-site; USD 176,000 - 304,000; Full time

Job summary

LILA Sciences is seeking a PhD-level researcher to advance computational materials science for organic electronics and photovoltaics. You will connect deep domain knowledge with practical discovery workflows, collaborating with AI researchers, software engineers, and experimental teams to accelerate design and optimization.

The role emphasizes first-principles methods, large-scale simulations, and machine learning to generate actionable hypotheses and drive closed-loop discovery across devices

Qualifications

• PhD or equivalent in materials science, chemistry, physics, or related field.
• Strong foundation in computational materials science and chemistry.
• Knowledge of electronic structure methods and large-scale simulations.
• Deep understanding of organic semiconductors and photovoltaics.
• Experience with first-principles and molecular simulations.
• Strong Python programming and scientific workflows.

Responsibilities

• Apply computational modeling and AI for materials discovery and design.
• Model charge transport and excited-state behavior.
• Connect simulations to experimental observations and develop workflows.
• Build predictive models to guide materials selection and optimization.
• Analyze data to generate actionable hypotheses.
• Collaborate with ML, software, and experimental teams.
• Communicate insights and limitations to collaborators.

Skills

Python

Materials science

Electronic structure

Atomistic simulations

AI/ML in materials

Education

PhD or equivalent

Job description

LILA Sciences is seeking a PhD-level researcher to advance computational materials science for organic electronics and photovoltaics. You will connect deep domain knowledge with practical discovery workflows, collaborating with AI researchers, software engineers, and experimental teams to accelerate design and optimization.

The role emphasizes first-principles methods, large-scale simulations, and machine learning to generate actionable hypotheses and drive closed-loop discovery across devices