AI Scientist - Representation Learning 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 building the capabilities to apply AI to every aspect of the scientific method.  We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at  www.lila.ai

If this sounds like an environment you’d love to work in, even if you only have some of the experience listed below, we encourage you to apply.

🌟 Your Impact at Lila

This new role in our Physical Science division focuses on creating, deploying and transforming state-of-the-art approaches for representation for diverse data structures related to materials science. You will partner with diverse teams at Lila, including materials science experts performing real-world experiments, to create novel data representations and algorithms for representation learning to capture fundamental structure and relationship across multi-modal data streams. By developing powerful new representation you will enable frontier materials discovery framework applied to real-world challenges.

🛠️ What You'll Be Building

• Physics-Informed, Multi-Modal Representation Learning Algorithms: Design and implement novel representation learning architectures for materials data spanning diverse materials applications and underlying data types.
• Self-Supervised & Unsupervised Learning Methods: Develop self-supervised and unsupervised learning approaches to discover meaningful materials representations and learned embeddings originating from diverse materials data streams.
• Real-World Validation & Deployment: Partner with materials scientists, chemist and software engineers to deploy algorithms and models for real-world materials design cases accelerating materials discovery.
• Cross-Functional Partnership: Work closely with R&D leadership, product managers, and automation specialists to translate scientific questions into modeling strategies, learning methods and deployment for materials discovery.

🧰 What You’ll Need to Succeed

• Proficiency in Python, deep learning frameworks and end-to-end workflow deployment for scalable learning algorithms.
• Understanding of state-of-the-art representation learning methods (self-supervised learning, unsupervised learning) and physics-informed inductive biases (geometric deep learning methods) and their applications to scientific problems, including materials science, chemistry, or biology (e.g. proteins).
• Elementary understanding of materials science, physics and chemistry and how relevant principles can be infused into architectures and learning algorithms.
• Strong self-starter and independent thinker, with strong attention to detail.
• Demonstrated industry experience or academic achievement.
• Excellent communication and presentation skills, capable of conveying technical information in a clear and thorough manner.
• Eager to work with highly skilled and dynamic teams in a fast-paced, entrepreneurial, and technical setting.

✨ Bonus Points For

• PhD in Materials Science, Computer Science, Physics, Chemistry, or related field with strong publication record in machine learning (NeurIPS, ICML, ICLR) and scientific (Nature, Science, Cell Press Matter, IOP) venues.
• Experience with computational materials science methods (DFT, Molecular Dynamics).
• Understanding of experimental materials science techniques related to synthesis and characterization.
• Experience in creating representation learning algorithms for uncommon data structures.

🌈 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 strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.