Interested in this AI/ML Engineer role at Progressive Leasing?
Apply Now →About This Role
Progressive Leasing is a leading provider of in\-store and e\-commerce lease\-to\-own solutions. With more than 20 years in FinTech, we’ve grown from start\-up to industry leader by innovating, simplifying, and valuing people. We are a subsidiary of PROG Holdings (NYSE: PRG), a FinTech holding company with three business segments: Progressive Leasing, Purchasing Power (a leading employee purchase program for consumer products and services using payroll deduction), and Four, a Buy Now Pay Later (BNPL) platform.
We are currently hiring an AI Workforce Enablement Lead to help support our enterprise AI transformation and ensure our people are set up to succeed as how work gets done evolves.
*This role is a work‑from‑home position and can be performed remotely anywhere in the continental US.*
*Employee Value Proposition (EVP):*PROG is dedicated to providing people with opportunity — opportunity for inclusive collaboration, opportunity for innovation, and opportunity for development.
WE ARE: The People team at PROG is lean, highly embedded with the business, and operating at the center of a company‑wide shift to AI‑enabled work. As workflows change, our focus is not just on tools — but on ensuring employees and managers actually adopt new ways of working in a practical, supported, and sustainable way.
We partner closely across Talent, HR, and Technology to redesign work thoughtfully and enable our workforce to move forward with clarity and confidence.
YOU ARE: A hands‑on workforce enablement and change execution leader who knows how to turn big transformation goals into real behavior change.
You’re not just building training or communications — you’re designing the systems, manager support, and enablement that help employees successfully transition as roles and workflows evolve. You’re comfortable operating with ambiguity, partnering closely with the business, and owning outcomes end‑to‑end.
This is an individual contributor role with real responsibility, autonomy, and impact.
YOUR DAY‑TO‑DAY:
- Own the workforce enablement approach for AI‑driven workflow and role changes
- Design and run enablement programs that drive real adoption — not “check‑the‑box” training
- Equip managers to lead teams through transition, including readiness, support, and tough conversations
- Translate redesigned workflows into clear role expectations, skills, and enablement plans
- Build practical toolkits, learning experiences, and manager resources that actually get used
- Partner across Talent, HR, and AI teams to ensure enablement comes before accountability shifts
- Track adoption using leading indicators (behavior change, readiness, friction), not vanity metrics
- Continuously refine approaches based on real signals from employees and managers
YOU’LL BRING:
- 5\+ years of experience in workforce transformation, people enablement, or talent‑side change execution
- Proven experience driving behavior change — not just delivering training or communications
- Comfort working inside or closely with HR/Talent systems (role design, skills, learning, performance)
- Strong judgment, autonomy, and follow‑through in fast‑moving environments
- Ability to diagnose resistance and tailor support appropriately
- Practical curiosity and experience with AI‑enabled ways of working (you don’t need to be an engineer)
- Bachelor’s degree or equivalent work experience
- AI‑enabled tools are already part of how work gets done at PROG, and their use will continue to expand. We value people who are curious, adaptable, and open to learning as roles and workflows evolve.
WE OFFER:
- Competitive Compensation
- Full Health Benefits; Medical/Dental/Vision/Life Insurance \+ Paid Parental Leave
- Company‑Matched 401(k)
- Paid Time Off \+ Paid Holidays \+ Paid Volunteer Hours
- Employee Resource Groups (Black Inclusion Group, Women in Leadership, PRIDE, Adelante)
- Employee Stock Purchase Program
- Tuition Reimbursement
- Charitable Gift Matching
- Job‑required equipment and services
*Progressive Leasing welcomes and encourages diversity in the workplace. We do not discriminate in any aspect of employment on the basis of race, color, religion, national origin, ancestry, gender, sexual orientation, gender identity and/or expression, age, veteran status, disability, or any other characteristic protected by federal, state, or local employment discrimination laws where Progressive Leasing does business.*
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 Progressive Leasing, 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.
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.
Progressive Leasing AI Hiring
Progressive Leasing has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span AZ, US, UT, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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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