Interested in this AI/ML Engineer role at FedEx?
Apply Now →About This Role
Provides leadership in the evaluation of strategic objectives, formulation of strategic direction, and influences decision making based on assessment of business trends and data analytics.
Essential Functions
- Plans, organizes, and develops recommendations grounded in deep understanding of the business, industry and market trends, consumer behavior, customer segments, and competitive strategy.
- Predicts changes in the current market and summarizes insights from proprietary and third\-party data and information to create distinctive insights.
- Develops and implements complex business analyses, manages business case development, and business\-at\-risk assessments. Develops research methodologies to understand key strategic issues, identifies data sources (internal and external) and develops complex opportunity models using statistical and business intelligence (BI) tools.
- Develops strategic frameworks and manages processes to build business scenarios using strategic thinking and decision science analysis.
- Provides a point of view on key strategic issues across major dimensions of the business – commercial, operations, customer segments, technology, etc.
- Engages with leadership teams/key stakeholders on identified priorities supported with clear fact based recommendations and provides actionable insights to leadership teams to recommend course of action.
- Provides thought leadership to cross\-functional teams working on strategic initiatives and acts as an internal consultant to senior management.
- Takes responsibilities to manage external consulting engagements with industry experts and takes on coaching and mentoring roles.
Perform other duties as assigned.
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Additional Details:
ARTIFICIAL INTELLIGENCE – AI STRATEGY FUNCTIONS
- Lead the AI enablement as a practice within portfolio operations, identifying high\-value use cases, standing up agent\-based capabilities, and partnering with Enterprise. Embed AI insights into across teams and workflows, improving decision quality and speed.
- Agentic AI \& Workflow Orchestration \- Designing and deploying autonomous AI agents. Turn manual marketing processes into automated agentic workflows.
- Architecture \& Integration – connecting APIs, event triggers, approval loops between AI tools and enterprise systems (CRM, CMS, or ERP).
- Providing updates to all levels of stakeholders to escalate risks and issues, updates, tracks, communicates status of initiatives through the demand lifecycle and monitors timing of release milestones.
- The ability to think strategically. Influencing business decisions using appropriate, fact\-based information synthesized from structured and unstructured data.
- Facilitate workshops, meetings, ceremonies, etc. as applicable to remove barriers, improve collaboration, mitigate risk, and ensure the free flow of information between all impacted stakeholders.
PORTFOLIO MANAGEMENT FUNCTIONS
- Lead the evolution of the Commercial Portfolio product development operating model through business agility constructs aligned to the Scaled Agile Framework.
- Partners with Demand Requestors/Initiative Owners, Enterprise Strategic Program Managers, Portfolio leads, Architecture teams, and Leadership to enable value\-based decision making and ensure alignment and prioritization of demand.
- Driving consistent use of operating model standards and tooling to ensure solid execution and accelerated value delivery. Leadership for the tools, dashboards and reporting to provide the right level of status, visibility to save time and drive positive outcomes.
- Collaborates with the enterprise tooling and data analytics teams to integrate into corporate systems, leverage common data sources and ensure data quality.
Location: Details:
This position is eligible for remote work and may be located anywhere within the United States excluding AK, HI and U.S. territories, however if you live within the 50 miles radius of a campus you will be required to work at a FedEx campus location several times per week.
Minimum Education
Bachelor’s Degree in a quantitative business field or closely related technical specialized field of study.
Minimum Experience
Seven (7\) years related work experience.
Work experience in functions focused on strategy development, competitive, customer, and industry analysis, market research, analysis of product performance, and support of corporate development/strategic value assessment activities.
Experience of supporting strategic assessments that are transformational in nature.
More advanced degrees may offset experience requirements. A related Master’s Degree equals two (2\) years experience.
Knowledge, Skills, and Abilities
Demonstrated business acumen including understanding the impact of technology trends on business.
Strong communication and presentation skills, problem structuring and problem solving skills.
Strong quantitative and analytical skills, including experience in databases, business intelligence (BI) tools, MS Office products and data modeling.
Strong human relations skills.
Preferred Qualifications:
Pay Transparency: CO: $8,007\.29/mo \- $17,393\.61/mo, CA: $8,452\.14/mo \- $14,413\.11/mo, NJ: $8,452\.14/mo \- $13,523\.42/mo, ME, OH \& VT: $8,452\.14/mo \- $15,124\.88/mo, MN: $8,452\.14/mo \- $16,637\.36/mo, IL \& NV: $8,452\.14/mo \- $17,393\.61/mo MD, NY, VA \& WA: $8,452\.14/mo \- $18,149\.85/mo, MA: $8,896\.99/mo \- $18,149\.85/mo, RI: $9,786\.68/mo \- $16,637\.36/mo, CT: $9,786\.68/mo \- $17,393\.61/mo, DC \& HI: $10,231\.53/mo \- $17,393\.61/mo, NYC: $10,231\.53/mo \- $18,149\.85/mo
Pay: USA: $8,007\.29/mo \- $18,149\.85/mo
Additional Details:
Pay Transparency:
The compensation listed reflects the pay range or rate of pay reasonably expected for this posted position at the posted location or locations. If this opportunity includes multiple job levels, the pay information represents the ranges for each level in that job family. Actual pay is determined by several job\-related factors permitted by law and relevant to the position, including, but not limited to, experience relative to the job, tenure, market level, pay at the location for this job, performance, schedule, and work assignment. In California, the compensation listed reflects the range or rate of pay reasonably expected for this posted position upon hire. In New Jersey, any compensable Security and Walk time will be paid to non\-exempt/hourly employees at the state minimum wage.
For details on our comprehensive benefits, click here.
Federal Express Corporation is an Equal Opportunity Employer including, Vets/Disability.
Reasonable accommodations are available for qualified individuals with disabilities throughout the application process. Applicants who require reasonable accommodations in the application or hiring process should contact [email protected].
Applicants have rights under Federal Employment Laws:
- Know Your Rights
- Pay Transparency
- Family and Medical Leave Act (FMLA)
- Employee Polygraph Protection Act
E\-Verify Program Participant: Federal Express Corporation participates in the Department of Homeland Security U.S. Citizenship and Immigration Services’ E\-Verify program (For U.S. applicants and employees only). Please click below to learn more about the E\-Verify program:
- E\-Verify Notice (bilingual)
- Right to Work Notice (English) / (Spanish)
Salary Context
This $96K-$217K range is below 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 FedEx, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($156K) sits 27% below the category median. Disclosed range: $96K to $217K.
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.
FedEx AI Hiring
FedEx has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Coraopolis, PA, US, Collierville, TN, US, Plano, TX, US. Compensation range: $162K - $217K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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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