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Computational Materials Science Jobs in Seattle, WA

Meta

Display Research Scientist

Redmond, WA · On-site

$154K - $217K/yr

... physics, materials science, electrical engineering, or a related field or equivalent practical ... Computational simulation experience with light including: ray-tracing, Fourier optics ...

Meta

Display Research Scientist

Redmond, WA · On-site

$154K/yr

PhD in optics, photonics, physics, materials science, electrical engineering, or a related field or ... Computational simulation experience with light including: ray-tracing, Fourier optics ...

Zap Energy

Senior/Principal Scientist (Theory)

Everett, WA · On-site

$130K - $160K/yr

... liquid metals and advanced materials, and compact, modular systems, Zap is building the ... Deploy computational tools for simulating compression physics, shock heating, turbulent and kinetic ...

Amazon

Sr. Applied Scientist, Amazon Ads

Seattle, WA · On-site

$104K - $142K/yr

This role offers unprecedented breadth in ML applications and access to extensive computational ... Criminal history may have a direct, adverse, and negative relationship with some of the material ...

Amazon

Senior Applied Scientist , Amazon Ads

Seattle, WA · On-site

$104K - $142K/yr

You'll have access to computational power and datasets most scientists only dream about. Build ... Criminal history may have a direct, adverse, and negative relationship with some of the material ...

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Computational Materials Science information

What is computational materials science?

Computational materials science is a field that uses computer-based simulations and modeling to understand, predict, and design the properties and behaviors of materials. Researchers use mathematical models, algorithms, and high-performance computing to study materials at the atomic, molecular, or macroscopic level. This approach allows scientists to accelerate the discovery of new materials, optimize existing ones, and investigate phenomena that may be difficult or expensive to study experimentally.

What are computational materials science jobs?

Jobs in computational materials science include academic and research positions in university settings. You can also find positions in the manufacturing industry. As a research scientist in computational materials science, your duties are to develop hypotheses and test them using computational modeling software and a variety of investigatory tools, such as Monte Carlo algorithms, density function theory, phase field models, and finite element methods. Your responsibilities include gathering data, testing modeling software, collaborating with other researchers to develop tools that aid them in their research, and analyzing data to write reports, journal articles, or presentations for conferences.

What are the key skills and qualifications needed to thrive as a computational materials scientist, and why are they important?

To thrive as a Computational Materials Scientist, you need a solid background in materials science, physics, or chemistry, often with a graduate degree and experience in scientific computing. Proficiency with simulation software (such as VASP, LAMMPS, or Quantum ESPRESSO), programming languages (like Python, C++, or Fortran), and familiarity with high-performance computing systems is typically required. Critical thinking, problem-solving abilities, and effective collaboration and communication skills set outstanding candidates apart. These competencies are crucial for designing, executing, and interpreting complex simulations and for translating computational insights into real-world materials innovations.

What are some common challenges faced by professionals in computational materials science, and how can they be addressed?

Professionals in Computational Materials Science often encounter challenges such as dealing with large datasets, managing the complexity of multi-scale simulations, and ensuring the accuracy of computational models. Addressing these challenges typically involves staying updated on the latest simulation software, collaborating closely with experimental teams to validate results, and developing strong programming and data analysis skills. Effective communication and interdisciplinary teamwork are also key, as projects often require input from chemists, physicists, and engineers to achieve successful outcomes.

What is the difference between Computational Materials Science vs Materials Engineer?

AspectComputational Materials ScienceMaterials Engineer
Required CredentialsTypically requires a PhD or Master's in materials science, physics, or chemistryBachelor's or Master's in materials engineering or related field
Work EnvironmentResearch labs, universities, or R&D departments focusing on simulations and modelingManufacturing plants, design offices, or product development teams
Industry UsagePrimarily in research, academia, and advanced R&D projectsProduction, quality control, and product development in manufacturing industries
Common Search/ComparisonYesYes

Computational Materials Science focuses on using computer simulations and modeling to understand and predict material behavior, often requiring advanced degrees. Materials Engineers work on designing, testing, and improving materials in practical applications, usually with a bachelor's or master's degree. While both roles are integral to materials development, Computational Materials Science is more research-oriented, whereas Materials Engineering emphasizes application and production.

More about Computational Materials Science jobs

What are the most commonly searched types of Computational Materials Science jobs in Seattle, WA?

The most popular types of Computational Materials Science jobs in Seattle, WA are:

What are popular job titles related to Computational Materials Science jobs in Seattle, WA?

For Computational Materials Science jobs in Seattle, WA, the most frequently searched job titles are:

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The top searched job categories for Computational Materials Science jobs in Seattle, WA are:

Computational Materials Science jobs near you
Infographic showing various Computational Materials Science job openings in Seattle, WA as of August 2026, with employment types broken down into 70% Full Time, 13% Part Time, and 17% Contract. Highlights an 90% In-person, and 10% Remote job distribution.

AI Research Scientist --Generative AI for Materials Discovery

Redmond, WA

Meta
Internet and IT • 10K+ employees

7.8

Company rating: 7.8 out of 10

Based on 45 frontline employees who took The Breakroom Quiz

139th of 246 rated software companies

People enjoy working here

Good employer

Recommended by students

Learn more about Metaexternal

$154K/yr

Full-time

Re-posted 14 days ago

Job description

Meta’s Reality Labs Research (RL-R) brings together a team of researchers, developers, and engineers to create the future of Mixed Reality (MR), Augmented Reality (AR), and Wearable Artificial Intelligence (AI). The Materials and Systems Innovation (MSI) group within Reality Labs Research creates and accelerates breakthrough materials and device technologies that unblock the path to low-cost, all-day wearable AR devices and advanced sensing and actuating systems for robotics. We identify key technology gaps requiring step-change innovation, build AI-driven autonomous discovery pipelines to compress development timelines, leverage external partners to accelerate research, and deliver high-quality technology solutions through cross-functional, high-performing teams.In this role, you will pioneer the application of generative AI to design novel compounds and molecular crystals, directly accelerating the discovery of next-generation materials for AR/VR devices and advanced robotic systems. Working at the frontier of deep generative modeling, computational chemistry, and agentic AI, you will develop and deploy state-of-the-art models — including diffusion models, flow matching, and transformer-based architectures — that predict and generate stable crystal structures and molecular candidates with target properties. Your work will be tightly integrated into our AI-driven autonomous discovery platform, collaborating with computational chemists and AI agent scientists to close the loop from molecular design to experimental validation.Together, we are going to build advanced prototypes, technologies, and toolsets that can advance how people interact with their surroundings. We invite you to join us as we work to bring these technologies from research to reality.
AI Research Scientist —Generative AI for Materials Discovery Responsibilities:
  • Develop, train, and deploy generative models (diffusion models, flow matching, variational autoencoders, transformer-based architectures) for molecular and crystal structure generation, property-conditioned design, and crystal structure prediction (CSP)
  • Design and implement reinforcement learning and alignment strategies (e.g., physics-informed reward signals from machine-learned interatomic potentials) to steer generative models toward physically stable and synthesizable candidates
  • Build foundational models and scalable pretraining pipelines that unify generative and predictive learning across molecules and crystalline materials, handling both discrete atom types and continuous 3D geometries
  • Collaborate closely with computational chemists to integrate first-principles calculations (DFT, force fields), molecular dynamics simulations, and domain-specific constraints into generative workflows
  • Partner with AI agent scientists to embed generative molecular design capabilities into LLM-based multi-agent systems, enabling closed-loop autonomous experiment planning, candidate generation, and decision making
  • Curate, preprocess, and manage large-scale molecular and crystal structure datasets for model training and benchmarking
  • Establish rigorous evaluation frameworks — measuring validity, novelty, uniqueness, stability, and synthesizability of generated structures — and benchmark against state-of-the-art methods
  • Contribute to the architecture and roadmap of the autonomous materials-discovery platform, ensuring generative design modules interface seamlessly with robotic workcells, characterization instruments, and data infrastructure

Minimum Qualifications:
  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • Ph.D. degree in Machine Learning, Computational Chemistry, Materials Science, Chemical Engineering, Physics, or a closely related technical field
  • 3+ years of research experience in generative modeling applied to molecular systems, crystal structures, or materials science (academic or industry)
  • Familiarity with large-scale molecular and crystal databases and data processing pipelines for chemical data
  • Demonstrated expertise in deep generative models — including diffusion models, flow matching / continuous normalizing flows, variational autoencoders, or autoregressive models — with applications to 3D molecular or crystal structure generation
  • Programming proficiency in Python with hands-on experience in PyTorch or JAX
  • proficiency in building, training, and evaluating large-scale deep learning models
  • Track record of first-author publications in top-tier ML or computational chemistry venues (e.g., NeurIPS, ICML, ICLR, JACS, Nature Computational Science, Digital Discovery)
  • Solid understanding of crystallography fundamentals— and molecular representations (molecular graphs, SMILES, 3D conformers)

Preferred Qualifications:
  • Experience integrating ML models into agentic AI frameworks or LLM-based multi-agent systems for autonomous scientific discovery
  • Hands-on experience with computational chemistry tools and simulation frameworks (DFT codes such as VASP/Gaussian, molecular dynamics with LAMMPS/OpenMM/ASE, force field development)
  • Experience with crystal structure prediction (CSP) pipelines, including lattice energy ranking and structure relaxation using machine-learned interatomic potentials
  • Demonstrated ability to collaborate across disciplines — bridging ML research with experimental chemistry, materials science, and software engineering teams
  • Experience building or fine-tuning foundation models (100M+ parameters) for chemical or materials domains, including multimodal architectures that jointly handle molecular graphs, 3D coordinates, and periodic lattice structures
  • Knowledge of geometric deep learning, equivariant neural networks, or graph neural networks for molecular property prediction
  • Familiarity with reinforcement learning or RLHF-style alignment techniques applied to molecular or materials generation

About Meta:
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.
Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.
Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com.
$154,000/year to $217,000/year + bonus + equity + benefits
Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.

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