Job description

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 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: sandia.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, TensorFlow, 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, TensorFlow, 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 versioning (Git, GitHub/GitLab 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 or hybrid modeling approaches. • Experience with high-performance computing (HPC) or cloud-based ML development (e.g., Slurm, Kubernetes, AWS/GCP). • Evidence of leadership potential through mentoring, collaboration, or open-source contributions. • Demonstrated competency with deep neural networks and mechanistic interpretability. • Fundamental understanding of probability theory and uncertainty quantification. • Strong background in statistics and probability theory, especially likelihood-based inference and the Fisher Information. • Solid foundation in linear algebra, including matrix decompositions, eigen decompositions, singular value decomposition, and spectral methods. • Familiarity with numerical linear algebra concepts, such as conditioning, low-rank approximation, iterative methods, and matrix factorizations. • Familiarity with high-dimensional statistical methods or inference for large parameter spaces, such as concentration inequalities. • Interest in developing theoretical foundations for machine learning models, particularly transformer architectures or neural networks. About Our Team: The AI Credibility team was created to respond to the national call to develop, deliver, and deploy AI to enable and accelerate science, engineering, and discovery across the Department of Energy mission areas. We are developing the team to represent the key capability area of AI Credibility at Sandia, harnessing a multidisciplinary group of leaders across many elements of this critical research area to advance the state of the art. Our team's mission is simple, but the research opportunities are vast: we will enable the use of AI in Sandia's high-consequence missions through the research, development, and application of credibility process methods for AI. Posting Duration: This posting will be open for application submissions for a minimum of three (3) calendar days, including the 'posting date'. Sandia reserves the right to extend the posting date at any time. Security Clearance: Sandia is required by DOE to conduct a pre-employment drug test and background review that includes checks of personal references, credit, law enforcement records, and employment/education verifications. Applicants for employment need to be able to obtain and maintain a DOE Q-level security clearance, which requires U.S. citizenship. If you hold more than one citizenship (i.e., of the U.S. and another country), your ability to obtain a security clearance may be impacted. Applicants offered employment with Sandia are subject to a federal background investigation to meet the requirements for access to classified information or matter if the duties of the position require a DOE security clearance. Substance abuse or illegal drug use, falsification of information, criminal activity, serious misconduct or other indicators of untrustworthiness can cause a clearance to be denied or terminated by DOE, resulting in the inability to perform the duties assigned and subsequent termination of employment. EEO: All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or veteran status and any other protected class under state or federal law. NNSA Requirements for MedPEDs: If you have a Medical Portable Electronic Device (MedPED), such as a pacemaker, defibrillator, drug-releasing pump, hearing aids, or diagnostic equipment and other equipment for measuring, monitoring, and recording body functions such as heartbeat and brain waves, if employed by Sandia National Laboratories you may be required to comply with NNSA security requirements for MedPEDs. If you have a MedPED and you are selected for an on-site interview at Sandia National Laboratories, there may be additional steps necessary to ensure compliance with NNSA security requirements prior to the interview date. Job ID: 699186 Job Family: 92 Regular/Temporary Position: T Full/Part-Time Status: F

Requirements

Must-haves

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, TensorFlow, 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, TensorFlow, 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 versioning (Git, GitHub/GitLab 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 or hybrid modeling approaches.
Experience with high-performance computing (HPC) or cloud-based ML development (e.g., Slurm, Kubernetes, AWS/GCP).
Evidence of leadership potential through mentoring, collaboration, or open-source contributions.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaimlllmpythondockerci/cdkubernetesawsgcp

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Benefits

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Vision

401k