Graduate Intern – Artificial Learning and Intelligence for Transportation Safety and Operations

National Laboratory of the Rockies • Golden, CO

6d • $44,500 - $71,200 • Onsite

About The Position

The Artificial Learning and Intelligent Systems Group in the Computational Science Center at the National Laboratory of the Rockies (NLR) has two openings for graduate student researchers supporting the Federal Highway Administration (FHWA) Office of Safety and Operations Research and Development (HRSO R&D) Super Intelligence (SI) Program. The interns will work with NLR researchers and FHWA program staff to support the program’s roadmap development, portfolio planning, technical communication, and applied AI research for traffic safety and operations.

The role includes scanning emerging AI technologies and literature, assessing their relevance and risks for FHWA, and developing and evaluating AI and machine-learning models for transportation operations and safety. Interns will process and integrate transportation datasets from multiple sources, validate findings against field data, write documented Python code, and use NLR high-performance computing systems as needed. They will also document methodologies and results and contribute to reports, presentations, and publications.

In addition to research, interns will help organize and characterize the SI program’s project portfolio, prepare briefings and other materials for FHWA leadership and stakeholders, and support program coordination by tracking tasks and progress, preparing meeting materials, and documenting decisions and action items. The position is located in Golden, Colorado. It is an internship with a fixed term, and the posting lists 40 hours per week. Candidates must meet the stated student or recent-graduate eligibility requirements and educational requirements before starting employment.

Requirements

• Minimum cumulative GPA of 3.0.
• Meet an eligible education status: enrolled full-time in an accredited bachelor’s or master’s degree program; have earned a bachelor’s or master’s degree within the past 12 months; or, for the Graduate + PhD category, have completed a master’s degree and be enrolled in an accredited PhD program.
• Meet educational requirements before the employment start date.
• Proficiency in Python for data analysis and machine learning, including tools such as pandas, NumPy, scikit-learn, PyTorch, or TensorFlow.
• Hands-on experience developing or applying modern AI methods, such as deep learning or large language models.
• Coursework or project experience in transportation or traffic engineering, computer science, or data science.
• Excellent written and verbal communication skills, including the ability to summarize technical work for non-technical audiences.
• Ability to work on a multi-organization team.
• Applicants must upload official or unofficial school transcripts as part of the application process.
• A letter of recommendation is required if selected for the position.

Nice To Haves

• Experience in optimization, including quantum computing, game theory, statics, and traffic-flow theory.
• Experience with traffic operations or safety data, such as detector, probe, trajectory, video, or crash data, or with Transportation Management Center operations.
• Familiarity with causal inference, anomaly detection, meta-learning, or synthetic data generation.
• Experience building applications with large language models, such as retrieval-augmented generation, agents, or automated document analysis.
• Experience supporting research programs through technology assessments, roadmaps, literature reviews, or policy and briefing documents.
• Familiarity with responsible or trustworthy AI concepts, such as AI risk management, model transparency, and data governance, in a public-sector context.
• Prior publications or presentations in transportation or computer science venues.
• Experience running code on high-performance computing systems and using Git.

Responsibilities

• Conduct technology and literature scans of emerging AI methods, such as large language models, foundation/world models, and agentic AI, and summarize their relevance, maturity, and risks for FHWA.
• Develop and evaluate AI and machine-learning models for traffic operations and safety, including traffic-flow prediction, speed-limit control, cooperative driving automation, and detecting, predicting, and explaining traffic anomalies and crashes.
• Process and integrate multimodal, multisource transportation datasets, such as sensor, video, trajectory, and incident data, and validate findings against field data.
• Write well-documented Python code and run workflows on NLR high-performance computing systems as needed.
• Document methodologies and results and contribute to technical reports, presentations, and publications.
• Help organize and characterize the SI program’s project portfolio, including inventories of projects, datasets, models, and outcomes, to inform planning and investment decisions.
• Prepare briefings, white papers, fact sheets, and presentation materials for FHWA leadership, stakeholders, and workshops.
• Support program coordination by tracking tasks and progress, preparing meeting materials, and documenting decisions and action items.

Benefits

• Medical insurance
• Dental insurance
• Vision insurance
• 403(b) Employee Savings Plan with employer match
• Sick leave where required by law
• Some positions may be eligible for relocation expense reimbursement
• Eligible employees may receive performance-, merit-, and achievement-based awards with a monetary component

What This Job Offers

• Career Level: Intern