Computational Materials Research Engineer

134,800.00-179,700.00 Business Function: R&D Engineering Work Location: San Jose Great Oaks Headquarters--LOC_WDT_USCA23 Company Description WD is building the infrastructure behind the AI-driven data economy. As AI scales, so does data. Every interaction, every model, every system generates data that must be stored, managed, and made accessible over time. That’s where we come in. We combine deep engineering expertise with global-scale manufacturing to deliver the storage systems that make AI possible, powering hyperscale data centers, cloud platforms, and enterprise infrastructure worldwide. This isn’t theoretical work. It’s real systems, at real scale, people solving some of the hardest challenges in technology today. We’re looking for people who want to build, solve, and operate at that level. Join us and let’s shape the future of data.

We are looking for a motivated individual to join Western Digital Research, a world-class research laboratory in San Jose, California with ~80 employees. Research activities in the lab focus on information storage, sensors, spintronics, superconducting devices, and AI memory/compute architectures. The materials team engages in experimental and computational research activity with the mission to develop novel materials for emerging technologies. The team is well-equipped with thin film deposition tools, related characterization methods and support staff, located in a fully functional cleanroom with the capability to fabricate a wide array of nanoscale devices using novel materials. Essential Duties and Responsibilities: Conducting and developing new capabilities in computational simulations of materials, interfaces and devices, including all forms of transport phenomena (e.g., thermal, electronic, spin). Work on AI assisted materials exploration and discovery with a focus on evaluating key material properties and interactions for device design. For this work, AI and machine learning techniques will be used to accelerate atomistic material simulations and to conduct rapid assessments of large material candidate pools. Work closely with experimental teams to guide material exploration and assist in interpreting experimental results. The work environment rewards innovative thinking and a highly collaborative attitude.

3+ years or higher experience in computational materials science with strong background in solid-state physics and devices. PhD in Physics, Materials Science, Electrical Engineering, Chemistry, or Chemical Engineering. Expert knowledge of atomistic simulation techniques such as density functional theory and classical molecular dynamics. Experience with ab-initio codes including QuantumATK, VASP, Quantum Espresso, Questaal, and KKR-CPA. Familiarity with PAOFLOW or WANNIER90. Experience with molecular dynamics codes such as LAMMPS. Experience using machine learned interatomic potentials for large scale atomistic simulations is a plus. Experience with broad computational material searches and machine learning techniques relevant for atomistic simulations. This includes, but is not limited to, machine learning neural networks for expedited prediction of material properties based on crystal structure features and DFT training sets. Experience in magnetism and magnetic materials, spin-orbit interactions, spin Hall materials, ordered and disordered materials, electronic, spin and thermal transport. Knowledge of chemical interactions at material interfaces and surfaces would also be an asset for a strong candidate. Experience predicting transport properties (e.g. spin Hall conductivity, anomalous Hall conductivity) from first principles using either non-equilibrium Green’s function techniques (NEGF) or Kubo-Greenwood formalism would also be a plus. Competent programming skills