Lila Sciences is looking to solve humankind's greatest challenges, such as human health, climate, and sustainability, by developing a scientific superintelligence platform and autonomous lab for life, chemistry, and materials science.
Requirements
- Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, JAX) and models in sparse, time-dependent data settings (few-shot learning, time-series prediction).
- Familiarity with materials datasets (experimental and/or computational) and performance characterization.
- Ability to collaborate across ML and materials science teams to deliver impactful methods and frameworks.
- Experience with time dependent data modeling methods.
- PhD (preferred) or equivalent experience in Materials Science, Applied Physics, Machine Learning, Computer Science or related fields.
Responsibilities
- Develop ML models to predict materials performance and reliability under diverse application conditions (e.g., stress, temperature, chemical environments, aging).
- Design data-efficient learning strategies for sparse, small, or incomplete experimental datasets.
- Integrate physics-informed priors, time-series prediction concepts, multi-modal methods and probabilistic modelling into predictive frameworks.
- Collaborate with materials scientists to curate, preprocess, and interpret complex experimental and simulation data.
- Build scalable ML workflows that can be deployed within Lilaâs platforms.
Other
- PhD (preferred) or equivalent experience in Materials Science, Applied Physics, Machine Learning, Computer Science or related fields.
- Commitment 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.
- Ability to collaborate across teams
- Lila Sciences is committed to equal employment opportunity
- Lila Sciences does not accept unsolicited resumes from any source other than candidates.
