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About This Role
Department: Technology
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:
Job Summary
Ready to lead the technology foundation that powers Southwest’s AI and enterprise intelligence strategy? As a Senior Manager Technology, you will lead the Intelligence Layer team, driving the delivery of trusted, scalable solutions that connect business knowledge, data, and technology across the enterprise. Partnering with business and technology leaders, you will help establish a unified foundation for analytics, AI, and decision-making while ensuring solutions align with enterprise strategy and customer needs. Your leadership will enable smarter insights, improve access to trusted information, and accelerate innovation across Southwest.
Additional details:
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.
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.
Model the Southwest Way
Guide and lead Team Members using SWA talent management practices to coach, develop, and engage Employees
Adhere to Company values and competencies to foster a Culture of inclusion, recognition, and support of a safe work environment
Invest in Leadership and self-growth through participating in continuous learning and development
Responsibilities
Lead, support, and influence the achievement of the mission, goals and objectives of the department and organization
Participate fully in organizational processes/initiatives; performance management, interviewing, budgeting, training, etc.
Lead the resolution of organization, team and individual issues, making appropriate decision
Develop and execute strategies and processes to improve performance and the value of products and services provided
Lead cross-functional Teams to deliver solutions and services to meet enterprise needs
Provide Technical thought Leadership related to assigned functional areas
Deliver technology solutions using best practices to achieve maximum Team productivity while delivering high quality products
Stay abreast of emerging trends, processes, and solutions related to assigned responsibilities
Build strong, collaborative relationships and credibility while influencing and driving change across respective Business and Technology Teams
Work with Technology and business partners to measure financial benefits and demonstrable business value
May perform other job duties as directed by Employee's Leaders
Knowledge, Skills and Abilities
Advanced knowledge of business/Leadership principals, including planning, organizing, directing, developing, and supervising
Advanced knowledge of personnel practices and regulations
Advanced knowledge of software development practices, concepts, methodologies, tools and trends
Advanced knowledge of project management methodologies
Skilled in excellent partnering, negotiation and communication in order to facilitate interactions with business Customers and technology teams
Ability to provide tactical leadership for Team(s)
Ability to use appropriate motivation and influence techniques to achieve desired Team results
Ability to set and manage stakeholder expectations
Ability to deliver quality projects on time and within budget
Ability to manage projects using the organization's project management methodology
Education
Required: High School Diploma or GED
Experience
Demonstrated experience in directly or indirectly leading team(s)
Fully functioning, broad knowledge in:
A Business and/or Information Technology environment
Leading teams and providing work direction
Preferred:
Experience with AWS cloud technologies
Databricks, including Unity Catalog
Experience in ontology engineering & semantic layer development
Experience with knowledge graph technologies, including Neo4j, Amazon Neptune, or similar platforms.
Licensing/Certification
N/A
Physical Abilities
Ability to perform work duties from [limited space work station/desk/office area] for extended periods of time
Ability to communicate and interact with others in the English language to meet the demands of the job
Ability to use a computer and other office productivity tools with sufficient speed and accuracy to meet the demands of the job
Other Qualifications
Must maintain a well-groomed appearance per Company appearance standards as described in established guidelines
Must be a U.S. citizen or have authorization to work in the United States as defined by the Immigration Reform Act of 1986
Must be at least 18 years of age
Must be able to comply with Company attendance standards as described in established guidelines
Must meet confidentiality expectations as to confidential, proprietary and sensitive Company information
Ability to work extended hours as needed
Must be able to travel and / or attend Company and non-Company facilities and remote locations such as remote-based offices as necessary
Pay & Benefits
Competitive market salary from $168,650 to $187,400 per year* depending on qualifications and experience. For eligible Leadership and individual contributor roles, additional bonus opportunities are available and awarded at the discretion of the company.
Benefits you’ll love
Fly for free, as a privilege, on any open seat on all Southwest flights (your eligible dependents too)
Southwest will help fund your Retirement Savings Plan with Company contributions up to 9.3% of your eligible earnings**
Potential for annual ProfitSharing contribution in the Southwest Retirement Savings Plan- when Southwest profits, you profit***
Competitive health insurance for you and your eligible dependents (including pets)
Southwest offers health plan coverage options that start from the very first day of employment. You will have 30 days to select and enroll in your health plan with coverage retroactively available to your first day of employment.
Explore more Benefits you’ll love: https://careers.southwestair.com/benefits
- Pay amount does not guarantee employment for any particular period of time.
**401(k) match contributions are subject to the plan’s vesting schedule and applicable IRS limits
***ProfitSharing contributions are subject to plan’s vesting schedule and are made at the discretion of the Company
Southwest Airlines is an Equal Opportunity Employer.
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Job Posting End Date
10/14/2026
Salary Context
This $168K-$187K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($178K) sits 16% below the category median. Disclosed range: $168K to $187K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
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: $187K - $187K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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