AI/ML Research Intern — Summer 2027

Notre Dame; San Francisco, Northern (CA, KY); Hybrid; USD 62,000 - 69,000; Full time

Benefits offered by this job

Stipend $45–$50/hour

On-site in San Francisco

Job summary

Gallup in San Francisco is offering a summer 2027 internship for students pursuing a degree in computer science, data science, or ML. You will research AI/ML advances, reproduce promising approaches, and design experiments to test AI-enabled products.

You will work on-site Monday through Thursday with remote Fridays, and there is potential for a full-time offer after graduation based on performance.

Qualifications

• Enrollment in a bachelor’s or graduate degree program in computer science, data science, machine learning, or related quantitative field.
• Programming experience relevant to machine learning or data science, including Python.
• Hands‑on experience building, training, evaluating, or experimenting with machine learning or AI systems through research, coursework, or substantial independent projects.
• Preferred experience designing experiments, analyzing quantitative results, or evaluating machine learning systems.
• Familiarity with modern machine learning frameworks and techniques such as PyTorch, transformer models, retrieval systems, or model evaluation preferred.
• Previous research experience, publications, open-source contributions, or technically substantial independent projects are a plus.
• Enrollment at a college or university within an 80-mile radius of San Francisco, California.
• Anticipated graduation between August 2027 and May 2028 preferred; 2029 graduates will be considered.
• Availability to work 40 hours per week for 12 weeks during summer 2027.
• Commitment to working on‑site at Gallup’s San Francisco office Monday through Thursday, with remote work option on Friday.
• Eligibility to work in the United States; this position does not sponsor employment visas.

Responsibilities

• Research advances in AI and machine learning, reproduce promising approaches, and evaluate improvements over existing methods.
• Design and contribute to experiments and prototypes testing new AI systems, applied machine learning, and AI-enabled products.
• Investigate technical questions involving AI systems, retrieval and knowledge systems, model evaluation, and machine learning relevant to Gallup’s AI research.
• Formulate hypotheses, conduct experiments, measure results, and provide technical recommendations.
• Analyze experimental results, identify limitations and unexpected findings, and communicate evidence clearly.
• Evaluate new models, methods, and tools through benchmarks, experiments, and implementation.
• Document methods, results, and recommendations for reproducibility and further development.
• Collaborate closely with AI researchers, engineers, and technical partners to transition capabilities from research to real systems.

Skills

Python

Research experience

Experiment design

Communication of results

Education

Bachelor's or Master's in CS/Data Science/ML

Tools

PyTorch

Transformer models

Retrieval systems

Model evaluation

Job description

Your Role

Here’s what you will be doing:

• Research advances in AI and machine learning, reproduce promising approaches, and evaluate improvements over existing methods
• Design and contribute to experiments and prototypes testing new AI systems, applied machine learning, and AI-enabled products
• Investigate technical questions involving AI systems, retrieval and knowledge systems, model evaluation, and machine learning relevant to Gallup’s AI research
• Formulate hypotheses, conduct experiments, measure results, and provide technical recommendations
• Analyze experimental results, identify limitations and unexpected findings, and communicate evidence clearly
• Evaluate new models, methods, and tools through benchmarks, experiments, and implementation
• Document methods, results, and recommendations for reproducibility and further development
• Collaborate closely with AI researchers, engineers, and technical partners to transition capabilities from research to real systems

About You

The company is looking for:

• Enrollment in a bachelor’s or graduate degree program in computer science, data science, machine learning, or related quantitative field
• Programming experience relevant to machine learning or data science, including Python
• Hands‑on experience building, training, evaluating, or experimenting with machine learning or AI systems through research, coursework, or substantial independent projects
• Preferred experience designing experiments, analyzing quantitative results, or evaluating machine learning systems
• Familiarity with modern machine learning frameworks and techniques such as PyTorch, transformer models, retrieval systems, or model evaluation preferred
• Previous research experience, publications, open-source contributions, or technically substantial independent projects are a plus
• Enrollment at a college or university within an 80-mile radius of San Francisco, California
• Anticipated graduation between August 2027 and May 2028 preferred; 2029 graduates will be considered
• Availability to work 40 hours per week for 12 weeks during summer 2027
• Commitment to working on‑site at Gallup’s San Francisco office Monday through Thursday, with remote work option on Friday
• Eligibility to work in the United States; this position does not sponsor employment visas

Compensation & Benefits

• Full-time summer 2027 internship paying $45 to $50 per hour
• Potential to work up to 20 hours per week during the school year depending on business demands
• Potential for a full-time job offer upon graduation based on business needs and intern performance

Training & Development

• Opportunities to grow skills through hands‑on research, experimentation, and exposure to AI systems, extensive data, and engineering challenges
• Work in an environment encouraging curiosity, rigorous thinking, and questioning existing approaches

Career Progression

• Potential for a full-time job offer upon graduation based on Gallup’s business needs and intern performance