Early Career Artificial Intelligence (AI) Data Science
Role details
Job location
Tech stack
Job description
- 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, explain ability, and auditability
- Document model assumptions, limitations, and operational dependencies
- Ensure compliance with data protection and organizational security policies
On any given day you may be called upon to:
- Prototype new AI workflows using frameworks like LangChain, Hugging Face, or OpenAI APIs
- Connect AI systems to enterprise data sources, dashboards, and collaboration tools
- Working with MLOps pipelines (e.g., MLflow, Kubeflow, or Vertex AI) for deployment and monitoring
- Evaluating new open-source models or vendor tools and testing their performance on internal data
- Collaborating with IT and cybersecurity teams to deploy AI tools securely
- Collaborating on public-private partnerships and multi-lab federated data efforts
- Creating documentation, tutorials, and reusable components to scale adoption
- Meeting with mission or program teams to identify where AI can streamline workflow
You will be part of a multi-disciplinary, mission-focused team delivering foundational data capabilities for transformative AI systems in national security, energy, and critical materials. Occasional travel may be required. If you are passionate about building the data backbone for next-generation AI at scale, we want to hear from you.
*Applicants on this posting may be interviewed by multiple organizations at Sandia National Laboratories.
**The selected applicant can work a combination of onsite and offsite. The selected applicant must live within a reasonable distance for commuting to the assigned work location when necessary. Salary Range
$102,400 - $199,700, 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.
Requirements
- A Bachelor's degree in a relevant STEM discipline such as Data Science, Statistics or an equivalent combination of directly relevant education and engineering or scientific experience that demonstrates the knowledge, skills, and ability to perform independent research and development.
- Ability to acquire and maintain a DOE Q clearance, * Graduate degree (M.S. or Ph.D.) in a relevant computationally-intensive discipline with a where an independent research project was a graduation requirement (e.g., independent project, thesis, or dissertation).
- Experience in developing software for enterprise and national security applications.
- Experience acquiring, preparing, and analyzing real world data.
- Demonstrated software development skills and familiarity with modern software development practices.
- Proven ability to work and communicate effectively in a collaborative and interdisciplinary team environment.
Also, for this posting we are seeking individuals with the following experience:
- Degree in Data Science, Informatics, Statistics, or a related STEM field with a significant data research component
- Background in AI-mediated data curation: automated annotation, feature extraction, and dataset certification
- Familiarity with data security and zero-trust principles, including secure enclaves, attribute-based access control, and data masking or differential privacy
- Familiarity with FAIR (Findable, Accessible, Interoperable, Reusable) data practices
- Experience implementing data governance and metadata management tools (e.g., Apache Atlas, DataHub, Collibra)
- Experience with programming languages, such as Python, R, SQL
- Working knowledge of a variety of machine learning concepts, techniques, models, and tools
- Familiarity with agile principles and practices
- Implementing data policies for classified, export-controlled, or proprietary data
- Advanced and automated data wrangling techniques for raw heterogeneous and streaming data sources, particularly for AI input
- Ability to obtain and maintain an SCI clearance, which may require a polygraph test
Benefits & conditions
*Salary range is estimated, and actual salary will be determined after consideration of the selected candidate's experience and qualifications, and application of any approved geographic salary differential.