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About This Role
Department: Customer & Brand Analytics
Our Company Promise
We are committed to provide our Employees a stable work environment with equal opportunity for learning and personal growth. Creativity and innovation are encouraged for improving the effectiveness of Southwest Airlines. Above all, Employees will be provided the same concern, respect, and caring attitude within the organization that they are expected to share externally with every Southwest Customer.
Job Description:
Summer 2027 Customer Experience and Analytics Data Science Internship
Notice : We ask that you apply with your personal email address rather than your school email (.edu) so we can stay connected about future roles at Southwest. Be sure to attach a resume to your application.
Customer Experience and Analytics Team (CX&A Data Science Intern): The CX&A Data Science Intern is responsible for the application of statistics/math, business acumen, and computer science concepts to collect, organize, analyze, summarize, and interpret data; or participate in the development of data science, machine learning, artificial intelligence, or optimization products which generate competitive advantage for the enterprise by driving actionable insights and decisions in support of descriptive, diagnostic, predictive, or prescriptive business needs.
Additional Details
Join Southwest as a Summer 2027 Intern, with a 12-week internship lasting from May 18 – August 8, 2027.
Pay of $22-26 per hour, corresponding with progress toward degree.
Stipends may be offered based on eligibility and program criteria.
The culture of Southwest Airlines means we value the camaraderie, collaboration, and innovation that occurs when we come together and interact face-to-face at our vibrant Corporate Campus. Due to the nature of this role, you would be required to work from our Dallas, TX headquarters facility during business hours (2702 Love Field Drive).
Dedicate 40 hours each week (maximum of 8 hours per day) to your internship responsibilities and tasks.
Receive free, unlimited space available travel privileges for yourself (taxes and fees may apply on international travel).
Interns will be provided any required equipment, such as a laptop, for the internship.
U.S. citizenship or current authorization to work in the U.S. required and no current or future work authorization sponsorship available.
We’re committed to fair hiring practices and to making employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, gender expression, national origin, age, military or veteran status, disability, genetic information, or other legally protected characteristics.
Duties and Responsibilities: The duties and responsibilities of the Customer Experience and Analytics (CX&A Data Science Intern) may include, but are not limited to:
Work on moderately complex data science, machine learning, AI, or optimization projects to define, analyze, and deliver solutions aligned with business objectives.
Apply math, statistics, and modeling methods for descriptive, diagnostic, predictive, and prescriptive analytics.
Extract, transform, and analyze large datasets to identify trends, risks, and opportunities, delivering actionable insights.
Support ongoing effectiveness and maintenance of models and analytical solutions.
Build and maintain relationships within the Customer Organization and contribute to the data science community.
May perform other job duties as directed by intern’s leaders.
Knowledge, Skills, and Abilities:
Proficiency in Microsoft Office Suite (Excel, Word, PowerPoint).
Strong Excel skills, including PivotTables, VLOOKUP, graphing, and data manipulation.
Ability to build and deliver PowerPoint presentations.
Strong written and verbal communication skills for technical and non-technical audiences.
Knowledge of foundational statistics and math concepts.
Understanding of descriptive, diagnostic, predictive, and prescriptive analytics.
Ability to compile, clean, transform, analyze, and visualize large-scale data.
Strong analytical and problem-solving abilities.
Ability to manage and organize large datasets accurately.
Detail-oriented with a focus on data integrity and process improvement.
Team-oriented with the ability to work independently.
Quick learner, self-motivated, and adaptable.
Ability to work independently and the willingness to function as a part of a Team.
Ability to coordinate multiple tasks and complete assignments with tight deadlines.
Must maintain a professional appearance and attitude
May perform other job duties as directed by the team.
Education:
High School Diploma, GED or equivalent education required
Progress toward a degree, major, or minor in Aviation, Aviation Management, Applied Mathematics, Analytics, Business Administration, Business Analytics, Business Administration, Computer Science, Data Analytics, Economics, Engineering, Finance, Hospitality, Information Science, Information Systems, Information Technology, Machine Learning, Management, Management Information Systems, Marketing, Math, Operations Research, Professional Sales, Science, Software Development, Statistics, Supply Chain or similar degree
Progress towards a 4-year undergraduate degree preferred
To be eligible, you must be currently enrolled as a full-time student (as defined by your university) at the time of application and during the internship
1-2 semesters remaining at the time the internship ends is preferred
Minimum 2.5 cumulative GPA at the time of application
Basic Qualifications:
Must be at least 18 years of age
You must have a valid U.S. Social Security Number to complete the full hiring process
Experience
Preferred: Experience with forecasting software and models.
Preferred: Programming knowledge in SQL, VBA, or other database management tools.
Preferred: Familiarity with Python, Java, or similar scripting languages.
Preferred: Experience with data visualization tools (Tableau, Power BI).
Preferred: Exposure to data cleansing and transformation tools (e.g., Alteryx).
Preferred: Knowledge of data science project lifecycle and stakeholder engagement.
Preferred: Ability to evaluate data science, machine learning, AI, or optimization techniques for business problems.
Preferred: Previous internship or work experience in analytics or aviation environment.
Preferred: Leadership experience or involvement in campus organizations.
Southwest Airlines is an Equal Opportunity Employer.
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Job Posting End Date
10/06/2026
Salary Context
This $45K-$54K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Southwest Airlines, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($49K) sits 76% below the category median. Disclosed range: $45K to $54K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Southwest Airlines AI Hiring
Southwest Airlines has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US. Compensation range: $54K - $54K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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