Postdoctoral Appointee- Machine Learning and Artificial Intelligence

Postdoctoral Appointee
- Machine Learning and Artificial Intelligence

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About Sandia

Sandia National Laboratories is the nation’s premier science and engineering lab for national security and technology innovation, with teams of specialists focused on cutting‑edge work in a broad array of areas. Some of the main reasons we love our jobs:

• Challenging work with amazing impact that contributes to security, peace, and freedom worldwide

• Extraordinary co‑workers

• Some of the best tools, equipment, and research facilities in the world

• Career advancement and enrichment opportunities

• Flexible work arrangements for many positions include work 80 hours every two weeks, with every other Friday off) andwork 4 ten‑hour days each week) compressed workweeks, part‑time work, and telecommuting (a mix of onsite work and working from home)

• Generous vacation, strong medical and other benefits, competitive 401k, learning opportunities, relocation assistance and amenities aimed at creating a solid work/life balance
* Learn more about Sandia at: http://(Use the "Apply for this Job" box below)..gov

What Your Job Will Be Like

We are seeking a Postdoctoral Appointee in Machine Learning and Artificial Intelligence to research, design, and deploy next‑generation agentic frameworks to accelerate scientific discovery, engineering design and advanced manufacturing and qualification strategies. The successful candidate will develop and deploy novel ML/AI architectures including generative, multi‑agent, and simulation‑informed models that can reason, plan, and collaborate in scientific contexts.

• Collaborate with domain experts across physics, manufacturing, and computational modeling to integrate AI solutions into complex simulation and scientific workflows.

• Contribute to codebases that implement modular, containerized (e.g., Docker/Singularity) ML solutions for distributed computing environments.

• Prototype AI frameworks that integrate simulation, optimization, and ML‑driven reasoning for scientific or engineering workflows.

• Develop and deploy machine learning pipelines in Python using frameworks such as PyTorch, Tensor Flow, or Hugging Face Transformers.

• Integrate state of the art and open source generative AI models into broader AI‑enabled workflows.

• Design, train, and evaluate large‑scale generative models or agentic architectures for scientific data synthesis and design tasks.

• Assist in defining and refining research vision, roadmaps, and success criteria for AI‑enabled multi‑agent frameworks.

• Conduct and publish novel research in generative AI, autonomous agents, or AI‑driven design acceleration.

• Present results through internal reports, external publications, and conferences.

• Mentor interns or junior researchers in applied AI development.

Due to the nature of the work, the selected applicant must be able to work onsite at least 75% of the time.

Qualifications We Require

• Ph.D. (earned within the past 5 years, or anticipated within 6 months) in Computer Science, Applied Mathematics, Mechanical Engineering, or related STEM discipline

• Ability to obtain and maintain DOE Q‑level security clearance

• Experience developing and deploying AI agents, multi‑agent frameworks, or LLM‑based tool use systems

• Proficiency in Python and experience with at least one major ML framework (e.g., PyTorch, Tensor Flow, JAX, Hugging Face Transformers)

• Working understanding of generative AI methods such as diffusion models, LLMs, or VAEs

• Strong analytical, written, and verbal communication skills

• Ability to work collaboratively in multi‑disciplinary research environments

Qualifications We Desire

• Demonstrated research experience in machine learning, deep learning, or artificial intelligence (e.g., publications, code repositories, or preprints)

• Experience with containerized workflows (Docker, Singularity) and software versioning (Git, Git Hub/Git Lab CI/CD)

• Exposure to modeling and simulation tools, scientific computing, or digital twin frameworks

• Familiarity with reinforcement learning, autonomous experimentation, or AI‑for‑science use cases

• Demonstrated ability to integrate ML with physics‑informed…