Postdoctoral Appointee: Machine Learning & Artificial Intelligence

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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 9/80 (work 80 hours every two weeks, with every other Friday off) and 4/10 (work 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*

World-changing technologies. Life-changing careers. Learn more about Sandia at: http://(Use the "Apply for this Job" box below)..gov

* These benefits vary by job classification.

What Your Job Will Be Like:

We are seeking multiple Postdoctoral Appointees in Machine Learning and Artificial Intelligence to contribute to fundamental, end-to-end AI-for-science research spanning theory, modeling, engineering, and operational deployment. Appointees will advance and integrate novel ML/AI architectures including generative, multi-agent, and simulation-informed models while combining physics-informed and data-driven approaches to accelerate scientific discovery, engineering, and mission work across diverse applications. Each hire will bring unique expertise in areas such as statistics, linear algebra, simulation, containerized workflows, HPC/cloud environments, and advanced AI solution skillsets, collaborating in multidisciplinary teams to advance impactful scientific outcomes and drive innovation in AI-for-science applications.

On any given day, you may be called on to:

Collaborate with multidisciplinary teams to conduct aligned AI research and integrate ML/AI solutions into complex scientific and engineering workflows.

Prototype and deploy machine learning pipelines using modern frameworks (e.g., PyTorch, Tensor Flow, Hugging Face Transformers) and containerized workflows for distributed computing environments.

Advance novel ML/AI architectures, including generative, multi-agent, and simulation-informed models, for scientific data synthesis, design, and optimization.

Apply rigorous statistical and probabilistic methods, including likelihood-based inference, Fisher Information, and uncertainty quantification, to develop credible and interpretable models.

Utilize advanced linear algebra techniques such as matrix decompositions, eigen decompositions, singular value decomposition, spectral methods, and high-dimensional statistical inference to advance research in AI.

Integrate machine learning with physics-informed or hybrid modeling approaches, and explore reinforcement learning, autonomous experimentation, and digital twin frameworks.

Contribute to research vision, roadmaps, and publish impactful scientific outcomes through internal reports, external publications, and open-source contributions.

Mentor interns or junior researchers and foster a collaborative, inclusive research environment.

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, Statistics, or related STEM discipline.

Ability to obtain and maintain DOE Q-level security clearance.

Qualifications We Desire:

Strong analytical, written, and verbal communication skills.

Ability to work collaboratively in multi-disciplinary research environments.

Demonstrated research experience in machine learning, deep learning, or artificial intelligence.

Experience developing and deploying LLM-enabled software, such as through direct use of LLM APIs, agents, or multi-agent frameworks.

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 Variational Autoencoders (VAEs).

Experience with…