Responsibilities

Design and implement state-of-the-art machine learning models (e.g., tree-based algorithms, fingerprint-based methods, multi-layer perceptrons, and graph neural networks) to predict chemical and material properties with unparalleled accuracy.
Develop and apply generative AI techniques to propose new molecules or materials with desired target properties that push past the limitations of today’s OLEDs.
Continuously refine and validate predictive models to improve accuracy and reliability ensuring they meet the highest standards for molecular property prediction and compound design.
Work closely with team members in the computational group to seamlessly integrate AI approaches with traditional chemical research methods such as laboratory experiments, molecular simulations, and quantum chemistry calculations.
Partner with other R&D scientists to interpret model predictions, guide experiment design, and identify promising leads for new compounds or materials.

Basic qualifications

PhD in Chemistry, Physics, or a related field (e.g., Chemical Engineering, Materials Science).
Proven experience applying machine learning to chemical problems, including developing both property prediction (inferential) models and generative models (demonstrated through peer-reviewed publications or successful practical applications).
Familiarity with a broad range of machine learning approaches relevant to chemistry, such as tree-based models, fingerprint-based similarity methods, multi-layer perceptrons (MLPs), and graph neural network models.
Proficiency in Python for scientific programming and data analysis, including experience with relevant libraries/frameworks (e.g., PyTorch, RDKit, PyG, etc.).
Excellent communication and collaboration skills, with the ability to work effectively in a multidisciplinary team environment.

Postdoctoral research experience (2+ years) or equivalent advanced research experience applying AI/ML in chemistry.
Background in organic electronics or related fields (e.g., OLED materials, organic semiconductors) is highly valued.
Experience with machine learned interatomic potentials (MLIP) including training and implementation is a plus.
Experience developing user interfaces or graphical tools for scientific software applications (GUI design skills).
Competitive base salary and annual bonus program
Medical/Prescription Drug coverage, Dental, and Vision for employees and family
Option between Flexible Spending account (FSAs) or Health Savings Account (HSA)
Group Term Life insurance, short term disability, and long-term disability benefits for employees
Employee Stock Purchase Plan (ESPP)
401(k) company contribution
Ewing Worldwide Headquarters (HQ) cafeteria provides breakfast and lunch to employees at no cost to them
Annual charitable matching gift
Generous Paid Time Off

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Fulltime Ai