Interested in this AI/ML Engineer role at UPS?
Apply Now →Skills & Technologies
About This Role
Before you apply to a job, select your language preference from the options available at the top right of this page.
Explore your next opportunity at a Fortune Global 500 organization. Envision innovative possibilities, experience our rewarding culture, and work with talented teams that help you become better every day. We know what it takes to lead UPS into tomorrow—people with a unique combination of skill \+ passion. If you have the qualities and drive to lead yourself or teams, there are roles ready to cultivate your skills and take you to the next level.
Job Description:
UPS Airlines is one of the largest international cargo airlines in the world servicing over 800 destinations worldwide. We are a Customer First, People Led, Innovation Driven company. Candidates will work primarily in an office environment with opportunities to travel and offer worldwide support. UPS Configuration Management engineers regularly interface with the software manufacturer, repair \& overhaul facilities, and other outside agencies. This position provides operational support and expertise to all Maintenance and Engineering departments. This role is critical to ensuring the safety, operational dispatch, efficiency, and reliability of our aircraft operations.
As an engineer in configuration management for UPS Airlines, you will develop solutions that support implementation of safety, reliability and airworthiness policies and procedures. You will have the opportunity to use your skills and judgment to ensure accurate aircraft configuration in Maintenix as well as work with internal customers and external vendors to improve overall quality, accuracy and efficiency of assemblies, tasks and processes with the system.
Responsibilities include, but are not limited to:
- Provide operational support for Maintenix, ensuring the system functions effectively and efficiently.
- Collaborate with the engineering department to provide expert guidance on aircraft maintenance and engineering projects.
- Support other airline departments with engineering expertise, including troubleshooting and resolving technical issues.
- Develop and implement maintenance strategies and procedures to enhance aircraft performance and safety.
- Analyze and interpret data from Maintenix to identify trends and recommend improvements.
- Ensure compliance with all regulatory requirements and company policies related to aircraft maintenance and engineering.
- Ensure accuracy of records and documentation in Maintenix and other companion systems.
- Participate in continuous improvement initiatives to optimize maintenance processes and practices.
- Support Maintenix software upgrades through implementation and testing of the software prior to installation into production environment.
- Assist internal customers to include but not be limited to; maintenance planning, aircraft materials, aircraft records, line maintenance and major maintenance with proper usage and set up of Maintenix.
- Working with UPS IT to develop, modify and test reports that work in concert with Maintenix to provide the business with regulatory and non\-regulatory information from Maintenix.
- Apply and monitor database rules within Maintenix to ensure the quality and validity of the established equipment baseline configuration for each aircraft.
Required Skills:
- Bachelor’s Degree – Mechanical, Electrical, or Aerospace engineering or other equivalent aviation degree or equivalent experience.
- Attention to detail and a commitment to maintaining high standards of safety and quality.
- Legally authorized to permanently work in the United States on a full\-time basis. Will not require sponsorship to maintain employment visa status.
Preferred Skills:
- Flexibility to travel both domestically and internationally.
- Proficiency in technical analysis and working knowledge of Federal Aviation Regulations or certification requirements.
- Prior Airline or Maintenance Repair Overhaul Facility experience.
- A strong Maintenix aptitude, be able to multi\-task, and have a hands\-on approach to projects.
Interpersonal Skills:
- Must have a positive attitude and the ability to work well with other team members.
- Must be able to demonstrate clear verbal and written communication skills.
Federal Aviation Administration required Drug Testing Information (FAA):
As part of the UPS pre\-employment process for a safety\-sensitive position, a drug screen is required. UPS Must receive a negative test result before you can be put into a safety sensitive position (14 CFR Part 120\.107\) Please be advised that you will be tested in accordance with 14 CFR Part 120\.109(a)(5\) and 14 CFR 120 Subpart E to determine the presence of marijuana, cocaine, opiates (including codeine, heroin\-6AM, morphine), opioids\-hydrocodone, hydromorphone, oxycodone \& oxymorphone, phencyclidine (PCP), and amphetamines/methamphetamines (including MDMA, MDA) or metabolites of these drugs.
Employee Type:
Permanent
UPS is committed to providing a workplace free of discrimination, harassment, and retaliation.
Employee Type:
Permanent
UPS is committed to providing a workplace free of discrimination, harassment, and retaliation.
Other Criteria:
UPS is an equal opportunity employer. UPS does not discriminate on the basis of race/color/religion/sex/national origin/veteran/disability/age/sexual orientation/gender identity or any other characteristic protected by law.
Basic Qualifications:
Must be a U.S. Citizen or National of the U.S., an alien lawfully admitted for permanent residence, or an alien authorized to work in the U.S. for this employer.
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At UPS, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
UPS AI Hiring
UPS has 6 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Denver, CO, US, Missouri City, TX, US, Tampa, FL, US. Compensation range: $29K - $87K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.