Early Career Data Science - Artificial Intelligence Enablement, NM​/CA Hybrid

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.

• 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 401(k), learning opportunities, relocation assistance and amenities aimed at creating a solid work/life balance*

* These benefits vary by job classification.

Sandia's artificial intelligence (AI) team is building the U.S. Department of Energy's (DOE) next‑generation AI Platform, an integrated scientific AI capability that delivers rapid, high‑impact solutions for national security, science, and applied energy missions. The Platform is based on three pillars:
Models, Infrastructure, and Data. You will join the Data Pillar team to design, implement, and operate Sandia's AI‑ready, zero‑trust data ecosystem. Your work will transform raw simulation outputs, sensor and facility logs, experimental records, and production data into governed, provenance‑tracked, and access‑controlled datasets that power AI models, autonomous agents, and mission workflows across DOE's HPC, cloud, and edge environments.

We anticipate multiple hires for the Data Pillar that collectively span the set of responsibilities and skills described below. Likewise, new hires will be expected to work in conjunction with existing Sandia staff and teams from other DOE laboratories to deliver on this ambitious, fast‑paced project. Importantly, we anticipate that while AI Platform development will leverage existing AI and data science tools extensively, success will also require considerable innovation and problem solving to address the unique needs of DOE applications.

If this sounds like an exciting challenge to you, we look forward to reading your application!

• AI Solution Development & Deployment
  • Design, prototype, and deploy AI‑driven applications that solve real organizational challenges
  • Integrate large language models (LLMs), computer vision, and other AI capabilities into production environments
  • Build and maintain APIs, pipelines, and interfaces that connect AI models to enterprise systems
• R&D Translation
  • Evaluate emerging AI tools, frameworks, and research from academia and industry
  • Rapidly prototype promising technologies to assess feasibility and value
  • Operationalize proven concepts into robust, user‑friendly systems
• Workflow & Automation Engineering
  • Build intelligent workflows that automate data processing, analysis, and decision support
  • Leverage orchestration tools and MLOps practices for reliable AI lifecycle management
  • Design systems that integrate human feedback and oversight where needed
• Collaboration & Enablement
  • Partner with data stewards to ensure clean, context‑rich data fuels AI solutions
  • Collaborate with domain experts to define use cases and success metrics
  • Provide guidance and templates that help other teams safely and effectively adopt AI tools
• Quality, Ethics, and Governance
  • Implement responsible AI principles, including bias testing, explainability, and auditability
  • Document model assumptions, limitations, and operational dependencies
  • Ensure compliance with data protection and organizational security policies

• Prototype new AI workflows using frameworks like Lang Chain, Hugging Face, or OpenAI APIs
• Connect AI systems to enterprise data sources, dashboards, and collaboration tools
• Work with MLOps pipelines (e.g., MLflow, Kubeflow, or Vertex AI) for deployment and monitoring
• Evaluate new open‑source models or vendor tools and…