Materials Informatics Engineer

Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, smart people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with Apple products. The same passion for innovation that goes into our products also applies to our practices strengthening our commitment to leave the world better than we found it. Join us to help deliver the next groundbreaking Apple product. Do you love working on challenges that no one has solved yet? As a member of our dynamic group, you will have the unrivaled and rewarding opportunity to craft upcoming products that will delight and inspire\\nmillions of Apple's customers every single day.

In this role, you will: develop and maintain AI/ML workflows for materials modeling\n- including surrogate models, generative material design, and closed-loop optimization frameworks that connect virtual material representations to FEA simulation and product-level performance targets\n

PhD in Materials Science, Chemical Engineering, Mechanical Engineering, Chemistry, Applied Physics, or a related field with a focus on computational or data-driven materials research\nStrong foundation in polymer physics and soft matter - viscoelasticity, rheology, structure-property relationships, and constitutive modeling\nProficiency in Python for scientific computing, including data pipelines, numerical modeling, and workflow automation\nDemonstrated experience applying ML to physical science problems - surrogate modeling, generative models (e.g.,VAEs), active learning, interpretable ML (e.g., SHAP), and\noptimization\nFamiliarity with molecular dynamics simulation (atomistic or coarse-grained) and/or computational chemistry methods for property prediction\nExperience with deep learning frameworks (PyTorch, TensorFlow, or JAX) for scientific and generative modeling\nAbility to independently drive research from problem formulation through implementation to actionable recommendations\nStrong communication skills with the ability to present technical work to cross-functional teams

Experience with materials informatics, cheminformatics, or polymer informatics (e.g., working with large polymer databases, SMILES/fingerprint representations, or group contribution methods)\nFamiliarity with finite element analysis tools (e.g., Abaqus) and constitutive model calibration for polymers\nExperience building or using LLM-based agents (e.g. Claude Code) for scientific workflows\nBackground in one or more application areas: adhesive materials, optical polymers, coatings, or display materials\nTrack record of publications or patents in computational materials science or applied ML for materials\nExperience with automated experimentation, high-throughput characterization, or self-driving lab concepts