Computational Materials Research Engineer
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 and familiarity with high performance computing environments. Experience coding in Python and working with material simulation environments like ASE would be highly beneficial. Excellent written communication and organizational skills. High attention to detail. Strong work ethic.
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Compensation & Benefits Details An employee’s pay position within the salary range may be based on several factors including but not limited to (1) relevant education; qualifications; certifications; and experience; (2) skills, ability, knowledge of the job; (3) performance, contribution and results; (4) geographic location; (5) shift; (6) internal and external equity; and (7) business and organizational needs. The salary range is what we believe to be the range of possible compensation for this role at the time of this posting. We may ultimately pay more or less than the posted range and this range is only applicable for jobs to be performed in California, Colorado, New York or remote jobs that can be performed in California, Colorado and New York. This range may be modified in the future. If your position is non-exempt, you are eligible for overtime pay pursuant to company policy and applicable laws. You may also be eligible for shift differential pay, depending on the shift to which you are assigned. You will be eligible to be considered for bonuses under either WD’s Short Term Incentive Plan (“STI Plan”) or the Sales Incentive Plan (“SIP”) which provides incentive awards based on Company and individual performance, depending on your role and your performance. You may be eligible to participate in our annual Long-Term Incentive (LTI) program, which consists of restricted stock units (RSUs) or cash equivalents, pursuant to the terms of the LTI plan. Please note that not all roles are eligible to participate in the LTI program, and not all roles are eligible for equity under the LTI plan. RSU awards are also available to eligible new hires, subject to WD's Standard Terms and Conditions for Restricted Stock Unit Awards.
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