Lila Sciences
AI Scientist - Generative Modeling for Materials Science
Lila Sciences, Cambridge, Massachusetts, us, 02140
Overview
AI Scientist - Generative Modeling for Materials Science
About Lila Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of boundless discovery by applying AI to every aspect of the scientific method and solving humankind's challenges in health, climate, and sustainability. Learn more at www.lila.ai.
Your Impact at Lila As a Research Scientist in our Physical Sciences organization, you develop state-of-the-art generative modeling techniques applied to critical challenges in materials science. You will work with cross‑functional machine learning experts, software engineers, and materials scientists across multiple teams to create and deploy generative models for Lila’s materials design challenges. You will see the impact of your methods on real-world materials in our experimental facilities.
What You'll Be Building
Generative Models: Design and implement generative models (diffusion models, flow-based models) and advanced sampling methods for diverse materials design challenges.
Data Representation: Develop architectures and methods using physics-informed constraints and domain knowledge to model materials across chemical space for real-world applications.
Real-World Validation & Deployment: Create and validate datasets, frameworks, and methods for validating generative models on experimentally realized materials; partner with software engineers and product managers to deploy solutions.
Cross-Functional Partnership: Work with R&D leadership, product managers, and automation specialists to translate scientific questions into data requirements and modeling strategies.
What You’ll Need To Succeed
Proficiency in Python, deep learning frameworks, and end-to-end workflow deployment.
Understanding of modern generative modeling methods (diffusion models, flow-based models, geometric deep learning) and their applications to scientific problems including materials science, chemistry, or biology.
Elementary understanding of materials science, physics, and chemistry to infuse principles into model design.
Strong self-starter, independent thinker, and attention to detail.
Demonstrated industry experience or academic achievement.
Excellent communication and presentation skills.
Willingness to work with highly skilled, dynamic teams in a fast-paced, entrepreneurial setting.
Bonus Points For
PhD in Materials Science, Computer Science, Physics, Chemistry, or related field with strong publication record.
Experience with computational materials science methods (DFT, Molecular Dynamics).
Understanding of experimental materials science techniques related to synthesis and characterization.
We’re All In Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or veteran status.
A Note to Agencies Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is prohibited unless contacted directly by Lila Sciences’ Talent Acquisition team. Any resume submitted by an agency without a signed agreement will become the property of Lila Sciences and no referral fees will be paid.
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About Lila Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of boundless discovery by applying AI to every aspect of the scientific method and solving humankind's challenges in health, climate, and sustainability. Learn more at www.lila.ai.
Your Impact at Lila As a Research Scientist in our Physical Sciences organization, you develop state-of-the-art generative modeling techniques applied to critical challenges in materials science. You will work with cross‑functional machine learning experts, software engineers, and materials scientists across multiple teams to create and deploy generative models for Lila’s materials design challenges. You will see the impact of your methods on real-world materials in our experimental facilities.
What You'll Be Building
Generative Models: Design and implement generative models (diffusion models, flow-based models) and advanced sampling methods for diverse materials design challenges.
Data Representation: Develop architectures and methods using physics-informed constraints and domain knowledge to model materials across chemical space for real-world applications.
Real-World Validation & Deployment: Create and validate datasets, frameworks, and methods for validating generative models on experimentally realized materials; partner with software engineers and product managers to deploy solutions.
Cross-Functional Partnership: Work with R&D leadership, product managers, and automation specialists to translate scientific questions into data requirements and modeling strategies.
What You’ll Need To Succeed
Proficiency in Python, deep learning frameworks, and end-to-end workflow deployment.
Understanding of modern generative modeling methods (diffusion models, flow-based models, geometric deep learning) and their applications to scientific problems including materials science, chemistry, or biology.
Elementary understanding of materials science, physics, and chemistry to infuse principles into model design.
Strong self-starter, independent thinker, and attention to detail.
Demonstrated industry experience or academic achievement.
Excellent communication and presentation skills.
Willingness to work with highly skilled, dynamic teams in a fast-paced, entrepreneurial setting.
Bonus Points For
PhD in Materials Science, Computer Science, Physics, Chemistry, or related field with strong publication record.
Experience with computational materials science methods (DFT, Molecular Dynamics).
Understanding of experimental materials science techniques related to synthesis and characterization.
We’re All In Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or veteran status.
A Note to Agencies Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is prohibited unless contacted directly by Lila Sciences’ Talent Acquisition team. Any resume submitted by an agency without a signed agreement will become the property of Lila Sciences and no referral fees will be paid.
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