AI Research Scientist —Generative AI for Materials Discovery

Meta; Pasadena (CA); On-site; USD 154,000 - 217,000; Full time

Benefits offered by this job

Bonus

Equity

Comprehensive benefits

Job summary

Meta’s Reality Labs Research is seeking a researcher to develop generative models for molecular and crystal structure generation. You will work closely with AI agents and computational chemists to innovate in materials science.

The ideal candidate has at least 3 years of experience in generative modeling, proficiency in Python (PyTorch or JAX), and a strong record of publications. A competitive compensation package is offered, including a salary range of $154,000 to $217,000 annually plus bonus and equity.

Qualifications

• 3+ years of research experience in generative modeling applied to molecular systems or materials science.
• Demonstrated expertise in deep generative models and applications to molecular or crystal structure generation.
• Programming proficiency in Python with experience in PyTorch or JAX.

Responsibilities

• Develop generative models for molecular and crystal structure generation.
• Collaborate with computational chemists and AI agents.
• Curate and manage large-scale molecular datasets for model training.

Skills

Generative modeling

Deep learning

Programming in Python

Data processing pipelines

Education

3+ years of research experience

Tools

PyTorch

JAX

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. We invite you to join us as we work to bring these technologies from research to reality.

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

• 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
• 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

Meta is proud to be an Equal Employment Opportunity and Affi­stantive 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 [e-mail].

$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.