Interested in this AI/ML Engineer role at Delta Air Lines?
Apply Now →About This Role
United States, Georgia, Atlanta
Crew Resources
29\-May\-2026
Ref \#: 32466
### How you'll help us Keep Climbing (overview \& key responsibilities)
The Product Owner of Crew AI Strategy and Delivery will be responsible for developing and executing a strategy for how predictive tools should inform decision making in Crew Resources from an analytical and technical perspective.
In this dynamic role, you'll collaborate with diverse squads across multiple business areas, gaining unparalleled cross\-divisional visibility. By working with various teams and navigating complex challenges, you'll become a well\-rounded Product Owner with a deep understanding of how interconnected efforts drive business success. This is your chance to elevate both your strategic impact and your professional growth in a fast\-paced, ever\-evolving environment.
We are seeking a highly adaptable, strategic, and detail\-oriented Product Owner to act as a key driver of product execution and alignment with enterprise priorities. This role requires a dynamic individual who can be deployed to various areas of the business based on organizational needs, delivering value by translating high\-level product strategy into actionable development steps.
As a centralized product owner, you will collaborate with multiple squads across the organization, using agile best practices to ensure a consistent, data\-driven approach to delivering product features. Additionally, you will foster upskilling opportunities across teams, elevating capabilities to support a culture of continuous improvement in a fast\-paced environment. This role is uniquely designed for a visionary Product Owner who thrives on complexity and cross\-functional challenges.
Responsibilities:
Strategic Product Execution
- Partner with product leaders to translate high\-level product strategy into executable backlogs, ensuring alignment with enterprise priorities and business objectives.
- Rapidly assess and understand key product features that will have a significant impact on assigned squads.
- Work with squads to craft clear and concise user stories with defined acceptance criteria.
- Manage the entire product lifecycle within development teams, facilitating delivery through continuous feedback loops with stakeholders.
- Lead sprint demos and ensure successful delivery of features that meet predefined business criteria.
- Ensure alignment between product vision and execution across different squads, coordinating with other Product Owners as necessary.
Cross\-Squad Collaboration and Leadership
- Lead and participate in all squad ceremonies (daily stand\-ups, sprint planning, backlog refinement, retrospectives).
- Facilitate backlog refinement meetings to ensure user stories are broken down into manageable increments.
- Act as the primary point of contact for integrating cross\-squad efforts, ensuring consistent product development.
- Foster a culture of continuous improvement by contributing to Product Owner communities of practice and sharing best practices.
Stakeholder Engagement
- Serve as the bridge between stakeholders and development squads, ensuring feedback is quickly integrated and business objectives remain aligned.
- Use a data\-driven approach to validate decisions and provide evidence\-based recommendations.
Agile Methodology Advocacy
- Champion agile practices to deliver high\-quality features.
- Utilize tools like Agility and Aha! to track progress and maintain data integrity.
- Promote flexibility and adaptability as priorities shift.
### Benefits and Perks to Help You Keep Climbing
Our culture is rooted in a shared dedication to living our values – Care, Integrity, Resilience and Servant Leadership – every day, in everything we do. At Delta, our people are our success. At the heart of what we offer is our focus on Sharing Success with Delta employees. Exploring a career at Delta gives you a chance to see the world while earning great compensation and benefits to help you keep climbing along the way:
- Competitive salary, industry\-leading profit sharing program, and performance incentives
- 401(k) with generous company contributions up to 9%
- New hires are eligible for up to 2\-weeks of vacation. This is earned for use in the following vacation year (April 1 – March 31\)
- In addition to vacation, new hires are eligible for up to 56 hours of paid personal time within a 12\-month period
- 10 paid holidays per calendar year
- Birthing parents are eligible for 12\-weeks of paid maternity/parental leave
- Non\-birthing parents are eligible for 2\-weeks of paid parental leave
- Comprehensive health benefits including medical, dental, vision, short/long term disability and life insurance benefits
- Family care assistance through fertility support, surrogacy and adoption assistance, lactation support, subsidized back\-up care, and programs that help with loved ones in all stages
- Holistic Wellbeing programs to support physical, emotional, social, and financial health, including access to an employee assistance program offering support for you and anyone in your household, free financial coaching, and extensive resources supporting mental health
- Domestic and International space\-available flight privileges for employees and eligible family members
- Career development programs to achieve your long\-term career goals
- World\-wide partnerships to engage in community service and innovative goals created to focus on sustainability and reducing our carbon footprint
- Business Resource Groups created to connect employees with common interests to promote inclusion, provide perspective and help implement strategies
- Recognition rewards and awards through the platform Unstoppable Together
- Access to over 500 discounts, specialty savings and voluntary benefits through Deltaperks such as car and hotel rentals and auto, home, and pet insurance, legal services, and childcare
### What you need to succeed (minimum qualifications)
- Proven experience as a Product Owner or similar role in a fast\-paced agile environment (4\+ years preferred).
- Expertise working in an agile organization.
- Ability to lead cross\-functional teams and manage shifting priorities.
- Strong stakeholder management skills and ability to explain technical concepts to non\-technical audiences.
- Excellent communication skills (written and verbal).
- Proficiency with Agile and roadmapping tools (e.g., Agility, Aha!).
- Strong analytical and problem\-solving skills with data\-driven decision\-making experience.
- Prioritizes safety and security of self, others, and data.
- Embraces diversity in people and perspectives.
- High school diploma/GED or equivalent.
- At least 18 years old with authorization to work in the U.S.
### What will give you a competitive edge (preferred qualifications)
- Experience in large\-scale enterprise environments with cross\-squad coordination.
- Familiarity with the airline industry.
- Experience in customer and employee journey optimization.
- Understanding of consumer behavior in digital environments.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Delta Air Lines, 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 in Demand for This Role
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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Delta Air Lines AI Hiring
Delta Air Lines has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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