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

millions of Apple's customers every single day.

Description

In this role, you will: develop and maintain AI/ML workflows for materials modeling

- including surrogate models, generative material design, and closed-loop optimization frameworks that connect virtual material representations to FEA simulation and product-level performance targets

Responsibilities:

Apply molecular dynamics simulation and computational chemistry methods to predict material properties, screen candidate chemistries, and guide experimental formulation

efforts

Build and deploy AI agents that automate the materials data pipeline - from vendor data ingestion and model fitting to simulation-ready material cards

Collaborate with external vendors on virtual materials frameworks, translating ML-driven insights into material specifications that reduce physical iteration cycles

Develop high-throughput screening pipelines for polymer discovery, integrating group contribution theory, chemistry language models, and property prediction to filter large

chemical spaces down to actionable candidates

Document methodologies, maintain shared codebases, and present findings to cross-functional partners in materials, FEA, and product design

Preferred Qualifications

Experience with materials informatics, cheminformatics, or polymer informatics (e.g., working with large polymer databases, SMILES/fingerprint representations, or group contribution methods)

Familiarity with finite element analysis tools (e.g., Abaqus) and constitutive model calibration for polymers

Experience building or using LLM-based agents (e.g. Claude Code) for scientific workflows

Background in one or more application areas: adhesive materials, optical polymers, coatings, or display materials

Track record of publications or patents in computational materials science or applied ML for materials

Experience with automated experimentation, high-throughput characterization, or self-driving lab concepts

Minimum Qualifications

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

Strong foundation in polymer physics and soft matter - viscoelasticity, rheology, structure-property relationships, and constitutive modeling

Proficiency in Python for scientific computing, including data pipelines, numerical modeling, and workflow automation

Demonstrated experience applying ML to physical science problems - surrogate modeling, generative models (e.g.,VAEs), active learning, interpretable ML (e.g., SHAP), and

optimization

Familiarity with molecular dynamics simulation (atomistic or coarse-grained) and/or computational chemistry methods for property prediction

Experience with deep learning frameworks (PyTorch, TensorFlow, or JAX) for scientific and generative modeling