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About This Role
Achieving our goals starts with supporting yours. Grow your career, access top\-tier health and wellness benefits, build lasting connections with your team and our customers, and travel the world using our extensive route network.
Come join us to create what’s next. Let’s define tomorrow, together.
Description
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United's Digital Technology team is comprised of many talented individuals all working together with cutting\-edge technology to build the best airline in the history of aviation. Our team designs, develops and maintains massively scaling technology solutions brought to life with innovative architectures, data analytics, and digital solutions.
Job Overview and Responsibilities
This Director – Customer AI Automation role will be responsible for establishing and leading United's Retail AI capabilities that transform the customer experience across all channels, with an immediate focus on voice and chat automation. This leader will oversee the strategy, roadmap, delivery, budget, and operational metrics of United's Retail AI Automation, including voice automation, and agentic workflows.
Our approach to automation will address real operational challenges \- for example, sharp spikes in customer contacts during large\-scale irregular operations that can lead to extremely long wait times for customers and significant additional hours for our frontline teams –\- while enabling our frontline experts to focus on the complex, high\-value moments where their judgment, empathy and problem solving skills make the greatest difference.
- Develop and execute a multi\-year Retail AI strategy with a strong emphasis on voice automation that aligns with United's customer experience goals, commercial vision, and operational resilience objectives
- Stay abreast of business strategy, goals, objectives, and operational performance across retail, digital, customer experience, and customer support domains
- Understand and provide recommendations for improvement of customer experience processes by applying AI capabilities, identifying improvements to existing systems, and defining new strategic AI capabilities that scale with demand
- Design and deploy AI systems that deliver fast, effortless resolutions and eliminate extremely long wait times, particularly during peak demand periods
- Guide teams in designing, building, testing, and deploying AI agents, interfaces, voice automation solutions, and personalization capabilities
- Partner closely with Customer Support leadership to ensure AI automation complements and enhance agent capabilities, freeing agents to focus on high\-value, complex customer interactions
- Establish and maintain strong business relationships with both senior and operating level business leaders to ensure effective delivery of business deliverables and AI technology solutions
- Educate business partners on AI capabilities, roles, responsibilities, processes, and work required to ensure effective delivery of new AI solutions, particularly voice automation
- Communicate strategy, goals, and measurable components to drive success and accountability across the organization, emphasizing customer satisfaction, operational efficiency, and agent empowerment
- Act as the point of contact and facilitate the work request demand and prioritization process for AI initiatives, balancing innovation with operational stability
- Oversee and lead projects to ensure delivery of work requests and projects are within committed budget, on schedule, and the quality of individual projects meets the overall department performance goals
- Advance, assist, and follow through on the resolution of issues related to the delivery of AI\-powered solutions
- Provide cost estimates, business case updates, and ROI analysis for AI initiatives, demonstrating impact on customer satisfaction, operational cost savings, and service scalability
- Identify needs, secure commitments and monitor progress of tasks from internal and external service provider teams, including AI technology vendors and voice platform providers
- Align with business partners on delivery success criteria and ensure AI solutions follow architectural standards, security protocols, and responsible AI principles
- Successfully integrate AI initiatives with other software applications in United's technology ecosystem
- Ensure adherence to code scanning, security protocols, and AI governance standards during releases
- Oversee the Retail AI engineering team and establish operational excellence, fostering a culture that values both technological innovation and human\-centered service design
- Remain informed of new AI technologies, voice automation frameworks, and best practices and incorporate those technologies to improve the productivity of the AI engineering team
Qualifications
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Minimum Qualifications:
- Bachelor's degree in Engineering, Business Administration, Computer Science, Information Technology or related field
- 8\+ years of relevant experience in Technology, or customer experience systems
- 5\+ years of management experience in an IT, or leadership related role
- Strong project organization skills, including prioritization, planning and organization
- Strong oral and written communication skills
- Strong AI industry trends and industry knowledge, particularly in voice automation
- Experience in management of remote teams across multiple locations, including offshore and third\-party service providers
- Prior experience in AI/ML platform development and deployment at scale
- Experience with cloud platforms (AWS preferred), modern software development practices, and Agile methodologies
- Experience building AI, chatbots, or voice automation solutions
- Must be legally authorized to work in the United States for any employer without sponsorship
- Successful completion of interview required to meet job qualification
- Reliable, punctual attendance is an essential function of the position
Preferred Qualifications:
- Master's degree
- 3\+ years of experience building Generative AI and AI platforms, with specific experience in voice automation
- Relevant experience in Technology, machine learning
- Prior experience with LangChain, LangGraph, or similar agent development frameworks
- Experience leading customer service technology delivery with deep knowledge of AI, voice automation tools, and IVR systems
- Experience designing AI solutions that integrate with contact center platforms and telephony systems
- Experience with A/B testing, experimentation frameworks, and AI model evaluation methodologies
- Knowledge of responsible AI principles, AI governance, and bias mitigation strategies
- Experience in travel, retail, or e\-commerce industry is a significant plus
- Familiarity with United's technology stack and SDLC tools
- Understanding of contact center operations, workforce management, and customer service metrics
The base pay range for this role is $175,750\.00 to $228,818\.00\.
The base salary range/hourly rate listed is dependent on job\-related, factors such as experience, education, and skills. This position is also eligible for bonus and/or long\-term incentive compensation awards.
You may be eligible for the following competitive benefits: medical, dental, vision, life, accident \& disability, parental leave, employee assistance program, commuter, paid holidays, paid time off, 401(k) and flight privileges.
United Airlines is an Equal Opportunity Employer. We recruit, employ, train, compensate, and promote without regard to race, color, religion, national origin, gender identity, sexual orientation, disability, age, veteran status, or any other protected category under applicable law. We provide reasonable accommodations for applicants and employees with disabilities. To request an accommodation, contact [email protected]
Salary Context
This $175K-$228K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At United 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($202K) sits 6% below the category median. Disclosed range: $175K to $228K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
United Airlines AI Hiring
United Airlines has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $153K - $228K.
Location Context
AI roles in Chicago pay a median of $192,900 across 197 tracked positions. That's 10% below the national 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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