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 a Senior Product Manager to help grow our company and ensure our mission is achieved! This role is work from home position and can be performed remotely anywhere in the continental US or in one of our corporate locations in Utah or Arizona.
WE ARE:
Prog Tech embodies the modernity and transformational vision that is core to our business evolution. As passionate and hungry technical experts, we join together on the mission of progressing through technology. We believe in taking pride in our engineering, in the relentless pursuit of daily progress, and to bring others with you in your march to the future. We continuously experiment, fail fast, and constantly deliver.
YOU ARE:
A highly skilled technical product manager with a background in managing exceptional digital storefront and commerce customer experience products. You have a passion for utilizing data to solve problems, innovate, and create products that delight our customers. You will have strong foundational product management skills and are a self starter. You are a strong communicator and are able to align your designer, engineering team, and stakeholders to deliver solutions on time.
YOUR DAY-TO-DAY:
- Demonstrated ability to solve difficult problems by bringing innovative solutions
- Use sound judgement to assess risks & opportunities, then provide well-structured, data-informed, and written narratives
- Proven ability to engage with engineers, designers, peers, and company leaders in a constructive and collaborative relationship
- Develop and maintain a product roadmap by meeting with internal stakeholders and customers to continuously improve the post-approval customer experience
- Lead cross functional initiatives to identify business problems, mitigate risk, and validate proposed solutions through customer and market research
- Define use cases, features, requirements, and acceptance criteria that articulate desired product functionality
- Establish in partnership with dependent teams an iterative release schedule that delivers value and features to customers, on time and as promised
- Own and optimize the digital storefront and commerce journey — merchandising, application, checkout, and post-purchase flows — using funnel and conversion data to prioritize
- Test, measure, and iterate on existing product experiences — identifying friction points and driving incremental improvements grounded in data
- Manage stakeholder expectations and communicate timelines, blockers, successes, and failures in a clear manner
- Analyze performance of product features
- Establish buy-in at all levels of the organization and maintain strong communication channels with various functional areas
YOU'LL BRING:
- 5 to 7 years of experience in software Product Management
- Experience owning and managing confidence building and trust products in a consumer-facing digital environment
- Direct experience in concepting and implementing agentic commerce solutions.
- Direct experience with B2B2C commerce platforms & shopping experiences.
- Hands-on commerce or digital storefront product experience — conversion optimization, confidence building, customer acquisition, and A/B testing across web and mobile
- Ability to design and iterate-on experiences that reduce customer effort and increase shop ability across web and mobile
- Experience configuring or optimizing AI chatbot workflows to support self-service customer resolution
- Familiarity with financial services or fintech products — comfortable navigating payment flows, lease or loan lifecycle management, and the regulatory context that comes with them
- Ability to move from high-level strategy to detailed execution
- Exceptional communication skills: listening, speaking, collaborating, and proven ability to write/present effectively to a variety of audiences
- High-integrity and ambition to relentlessly pursue the best possible product and service
- Demonstrated ability to self-motivate, self-direct, and be flexible within a fast-paced environment
- Passion and desire for innovating exciting new products
- Bachelor's degree or equivalent work experience
YOU MIGHT ALSO HAVE:
- Payment or billing product experience
- Experience with commerce platforms or merchant storefront integrations (hosted checkout, APIs, SDKs, or plug-ins)
- Experience with customer notification systems — push, email, or SMS triggers tied to account or payment events
- Experience in lease-to-own, BNPL, or other consumer lending / FinTech products
- Mobile & web-based product experience
- Risk product experience
WE OFFER:
- Competitive Compensation
- Full Health Benefits; Medical/Dental/Vision/Life Insurance + Paid Parental Leave
- Company Matched 401k
- 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. 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: $229,300; Director: $272,300; VP: $235,830.
Progressive Leasing AI Hiring
Progressive Leasing has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Diego, CA, US.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $263,911, 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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