Postdoctoral Appointee: Machine Learning & Artificial Intelligence

• Job
• Location:
  
  Albuquerque, NM
• Full/Part Time:
  Full-Time
• Regular/Temporary:
  Temporary

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.

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.

• 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.

• 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 containerized workflows (Docker, Singularity) and software…