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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.
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Description
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 role will drive architecture, development, and operations of our ML engineering and GenAI systems, enabling scalable and responsible AI solutions across the business.
This role requires a deep understanding of ML infrastructure and MLOps, combined with hands-on or architectural experience in LLMs, RAG pipelines, and GenAI application integration
The position involves leading a team of ML engineers and collaborating cross-functionally with Data Science, Data Engineering, DevOps, and business units to deliver impactful AI outcomes at scale.
- Strategic Leadership & Platform Ownership: + Define and execute the ML/GenAI platform strategy aligned with enterprise digital transformation objectives + Hands-on experience leading a Generative AI and AI Agents + Own the platform roadmap, architecture decisions, and budget planning to scale AI capabilities across the enterprise + Collaborate with CDO, CIO, and senior stakeholders to identify, prioritize, and fund impactful AI/GenAI investments + Represent the ML Center of Excellence (COE) in cross-functional meetings and strategic planning forums + Communicate strategy, progress, and outcomes to executive stakeholders through clear presentations and business narratives + Serve as the primary liaison between the COE and business units, effectively communicating technical capabilities and business impact
- GenAI & LLM Strategy: + Lead initiatives around LLMs and foundation models (e.g., OpenAI, Anthropic, and Hugging Face) + Design and operationalize GenAI pipelines (e.g., RAG, prompt orchestration, fine-tuning, and safety guardrails) + Work with AI engineers to help them drive the architecture, design, and implementation of key components of the Agentic AI and Machine Learning platform + Build and deploy secure, scalable GenAI applications with a strong emphasis on privacy, safety, and compliance + Integrate LLMs into enterprise workflows, such as copilots, document summarization, intelligent assistants, and domain-specific Q&A systems + Partner with business leaders to identify opportunities where Agentic AI and Machine Learning can create measurable value. Translate business needs into clear AI solution designs, including guardrails, validation approaches, and measurable success metrics
- GenAI Engineering & AIOps: + Design, manage, and monitor the enterprise AI engineering platform, ensuring scalability, reliability, and automation + Take ownership of observability, and resilient architecture + Develop robust AIOps processes to monitor model performance, detect drift, and automate retraining and validation + Build and maintain tools and frameworks to govern GenAI models for compliance, bias, versioning, traceability, and auditability
- Data Engineering & Feature Platforms: + Design and implement feature engineering and data pipelines to deliver high-quality training data and inference-ready datasets + Partner with data scientists and engineers to create reusable, production-grade feature stores and pipelines + Solve complex data ingestion, transformation, and governance challenges in collaboration with data platform and DataOps teams + Develop integrated ML/AI solutions on enterprise analytics platforms
- Team Leadership & Talent Development: + Hire, mentor, and grow a high-performing AI engineering team with a focus on innovation, execution, and impact + Provide technical mentorship and guidance to AI engineers and data scientists, ensuring high standards in design and implementation + Promote a culture of continuous learning, experimentation, and operational excellence.
- Building auto-scaling ML systems: + AI Engineering & AIOps
- MLflow, KServe, SageMaker, Vertex AI, Databricks, etc. + GenAI
- LLM providers (OpenAI, Anthropic), Hugging Face, LangChain, Agentic Frameworks, Agent Evaluations, etc. + Data Infra
- Spark, Kafka, Delta Lake, etc + DevOps
- Kubernetes, Jenkins, GitOps, Terraform, etc
Qualifications
What’s needed to succeed (Minimum Qualifications):
- Bachelor's degree in Computer Science, Data Science, Engineering or related field
- 5+ years of experience leading product or technical teams and delivering large-scale initiatives
- 2+ years of Gen AI experience
- Proven experience guiding cross-functional teams through complex, multi-stakeholder programs
- 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
What will help you propel from the pack (Preferred Qualifications):
- Master's degree
- 10+ years of experience delivering large-scale initiatives
The base pay range for this role is $147,060.00 to $191,516.00.
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 $147K-$191K range is below the median for AI/ML Engineer roles in our dataset (median: $177K across 1606 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 9,253 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 13,052 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,382. This role's midpoint ($169K) sits 20% below the category median. Disclosed range: $147K to $191K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $264,000. 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: $229,382; Director: $272,300; VP: $235,830.
United Airlines AI Hiring
United Airlines has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Chicago, IL, US, Chicago, IL. Compensation range: $191K - $191K.
Location Context
AI roles in Chicago pay a median of $193,440 across 429 tracked positions. That's 9% 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 9,253 open positions tracked in our dataset. By seniority: 107 entry-level, 4,462 mid-level, 3,410 senior, and 1,274 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (530 positions). The remaining 8,653 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $264,000. 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 9,253 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,989), AI Software Engineer (462), AI Product Manager (457). 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 (107) are outnumbered by mid-level (4,462) and senior (3,410) 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 1,274 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (530 positions), with 8,653 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 $264,000, 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 (1,638 postings), Aws (893 postings), Azure (718 postings), Rag (666 postings), Gcp (532 postings), Pytorch (434 postings), Claude (408 postings), Kubernetes (402 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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