Machine Learning Scientist, Materials Performance Modeling

Machine Learning Scientist, Materials Performance Modeling

Cambridge, MA USA

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 introducingscientific superintelligence to solve humankind's greatestchallenges, 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.

As a Machine Learning Scientist focused on Materials Performance Modeling, you will develop and apply state-of-the-art ML methods to predict how materials behave under real-world application conditions. You will tackle the challenges ofsparse, noisy, and heterogeneous scientific datasets, creating robust models that accelerate the design and validation of novel materials. By combining deep learning with physics-informed and data-efficient approaches, your work will directly advance Lila’s mission of building an autonomous scientific superintelligence.

What You'll Be Building

• Develop ML models to predictmaterials performance and reliabilityunder diverse application conditions (e.g., stress, temperature, chemical environments, aging).
• Designdata-efficient learning strategiesfor sparse, small, or incomplete experimental datasets.
• Integratephysics-informed priors, time-series prediction concepts, multi-modal methods and probabilistic modellinginto predictive frameworks.
• Collaborate with materials scientists tocurate, preprocess, and interpretcomplex experimental and simulation data.
• Build scalable ML workflows that can be deployed within Lila’s platforms.

What You’ll Need to Succeed

• PhD (preferred) or equivalent experience inMaterials Science, Applied Physics, Machine Learning, Computer Science or related fields.
• Strong proficiency inPythonand modern ML frameworks (PyTorch, TensorFlow, JAX) and models in sparse, time-dependent data settings (few-shot learning, time-series prediction).
• Familiarity withmaterials datasets(experimental and/or computational) and performance characterization.
• Ability to collaborate across ML and materials science teams to deliver impactful methods and frameworks.
• Experience withtime dependent data modelingmethods.

Bonus Points For

• Experience withphysics-informed MLorhybrid physics/ML approaches.
• Familiarity withmultimodal data integration(e.g., combining simulation, imaging, spectroscopy, and tabular data).

Lila Sciences iscommitted to equal employment opportunityregardless 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.

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