Flagship Ventures
ML Scientist - Materials Performance Modeling
Flagship Ventures, Cambridge, Massachusetts, us, 02140
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
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 of
sparse, 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 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 Lila's platforms.
What You'll Need to Succeed
PhD (preferred) or equivalent experience in
Materials Science, Applied Physics, Machine Learning, Computer Science or related fields . 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.
Bonus Points For
Experience with
physics-informed ML
or
hybrid physics/ML approaches . Familiarity with
multimodal data integration
(e.g., combining simulation, imaging, spectroscopy, and tabular data).
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. #J-18808-Ljbffr
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
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 of
sparse, 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 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 Lila's platforms.
What You'll Need to Succeed
PhD (preferred) or equivalent experience in
Materials Science, Applied Physics, Machine Learning, Computer Science or related fields . 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.
Bonus Points For
Experience with
physics-informed ML
or
hybrid physics/ML approaches . Familiarity with
multimodal data integration
(e.g., combining simulation, imaging, spectroscopy, and tabular data).
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. #J-18808-Ljbffr