Postdoctoral Appointee - Artificial Intelligence Data Science - Hybrid

Sandia • Albuquerque, NM

72d • Hybrid

About The Position

Sandia's AI team 1466 is building DOE's next-generation AI Platform around three pillars Data, Models, and Infrastructure to solve high-impact "lighthouse problems" in agile deterrence, energy dominance, and critical minerals. As a Postdoctoral Appointee, you"ll 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.

Requirements

• Possess, or are pursuing, a PhD in Computer Science, Data Science, Statistics, or a related science or engineering field, PhD must be conferred within five years prior to employment
• Experience or knowledge in building and maintaining production data pipelines (ETL/ELT) and data warehouses or data lakes
• Proficiency in programming languages such as Python, SQL, and experience with frameworks like Apache Spark or Dask
• Understanding of data security and zero-trust principles, including secure enclaves, attribute-based access control, and data masking or differential privacy
• Familiarity with cloud platforms (AWS, Azure, or GCP) and container orchestration (Kubernetes)
• Ability to acquire and maintain a DOE Q-level security clearance

Nice To Haves

• Significant data research experience
• Background in AI-mediated data curation: automated annotation, feature extraction, and dataset certification
• Experience implementing data governance and metadata management tools (e.g., Apache Atlas, DataHub, Collibra)
• Experience developing and refining data architectures and data flows
• Hands-on background in MLOps and CI/CD for data and ML workflows (e.g., Jenkins, GitLab CI, MLflow)
• Knowledge of human-factors engineering and UX design principles for data platforms
• Knowledge of agile principles and practices and experience working as part of agile teams
• Ability to work effectively in a dynamic, interdisciplinary environment, guiding technical decisions and mentoring junior staff
• Strong written and verbal communication skills, with the ability to present complex data concepts to diverse audiences
• Ability to obtain and maintain a SCI clearance, which may require a polygraph test

Responsibilities

• Build and operate an AI-Ready Lakehouse
• Design and maintain a federated data lakehouse with full provenance/versioning, attribute-based access control, license/consent automation, and agent telemetry services
• Implement automated, AI-mediated ingestion pipelines for heterogeneous sources (HPC simulation outputs, experimental instruments, robotics, sensor streams, satellite imagery, production logs)
• Enforce Data Security & Assurance
• Develop a Data Health & Threat program: dataset fingerprinting, watermarking, poisoning/anomaly detection, red-team sampling, and reproducible training manifests
• Configure secure enclaves and egress processes for CUI, Restricted Data, and other sensitive corpora with attestation and differential-privacy where required
• Define and Implement Data Governance
• Establish FAIR-compliant metadata standards, data catalogs, and controlled-vocabulary ontologies
• Automate lineage tracking, quality checks, schema validation, and leak controls at record-level granularity
• Instrument AI Workflows with Standardized Telemetry
• Deploy Agent Trace Schema (ATS) and Agent Run Record (ARR) frameworks to log tool calls, decision graphs, human hand-offs, and environment observations
• Treat agent-generated artifacts (plans, memory, configurations) as first-class data objects
• Collaborate Across Pillars
• Work with Models and Interfaces teams to integrate data services into training, evaluation, and inference pipelines
• Partner with Infrastructure engineers to optimize data movement, tiered storage, and high-bandwidth networking (ESnet) between HPC, cloud, and edge
• Engage domain scientists and mission leads for agile deterrence, energy grid, and critical minerals use cases to curate problem-specific datasets
• Support Continuous Acquisition & Benchmarking
• Design edge-to-exascale data acquisition systems with robotics and instrument integration
• Develop data/AI benchmarks—datasets, tools, and metrics—for pipeline performance, model evaluation, and mission KPIs

Benefits

• Generous vacation
• Strong medical and other benefits
• Competitive 401k
• Learning opportunities
• Relocation assistance
• Amenities aimed at creating a solid work/life balance

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Industry: National Security and International Affairs
• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees