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About This Role
Location: Remote
Join American Residential Services, one of the nation’s largest providers of residential HVAC, plumbing, and electrical services with 7,000 team members and more than 50 years of experience. With 70+ locations in 24 states, coast-to-coast, we are growing and hiring professionals to help support our branch offices, field trades, call centers, and corporate office.
The Senior Manager, AI Product leads some of ARS’s flagship AI-first products. These enterprise-scale initiatives apply machine learning, AI, and AI agents to how we serve our customers and drive profitable growth across the enterprise. This role reports to the VP, Data & Applied Intelligence (D&AI) and owns the measurable business outcomes of each flagship program, from strategy and business case through production adoption and value realization.
You’ll serve as:
- Product lead for D&AI’s flagship AI products, beginning with AI-driven dispatch optimization for field service
- Outcome owner accountable for driving, measuring, and reporting the realized business value of each flagship
- Strategic partner to business leaders across operations, call center, marketing, finance, and IT who are partners in execution
This role works cross-functionally with data engineering, data science, platform, business teams, and external implementation partners to turn complex operational problems into AI products that people trust, adopt, and that deliver measurable financial impact.
- Own product strategy, roadmap, and business case for a portfolio of flagship AI products, aligned to ARS’s enterprise transformation initiatives such as dispatch, call center, pricing and discounting, membership, marketing and conversion, and fleet and capital efficiency
- Define product vision, release plans, and success criteria for each flagship
- Translate operational problems into product requirements, success metrics, and acceptance criteria that data science and engineering teams can build against
- Arbitrate scope, sequencing, and trade-offs with operations, branch, finance, and IT; manage intake, backlog, releases, and dependencies across concurrent programs
- Own value measurement for each flagship: baselines, KPI definitions, experiment design (A/B tests and branch-level pilots), and attribution of benefit to the right business lever
- Lead pilot design, launch readiness, and scaled rollout, planning field-facing releases around seasonal peak-demand periods
- Own adoption and change management for each product including training, communication, and feedback loops with the business users it serves
- Guide decisions on where AI acts on its own and where people stay in the loop, weighing risk, accuracy, customer impact, and value
- Manage implementation partners and vendors; support build-vs-buy evaluations, statements of work, and renewals
- Ensure responsible AI: explainable recommendations, human override, model monitoring, and data governance, privacy, and compliance requirements are met
- Communicate progress, value, and risk to executive leadership in clear, business-first terms
- Build the AI Product function over time — practices, templates, and team — as the portfolio grows
Success Measures:
- Flagship products delivered on the committed roadmap, with clear visibility into priorities, status, and risk
- Measured, auditable business value (revenue, margin, and cost) attributed to each flagship against an agreed baseline
- Strong adoption and sustained use across the business operations and functions each product serves
- Business owners and executive sponsors aligned on scope, sequencing, and success criteria
- A repeatable AI product playbook (discover, pilot, scale, measure) applied across flagships
Core Competencies:
- Strategic Vision – Connects a multi-year AI roadmap to near-term, value-producing releases
- Accountability – Owns business outcomes, not just delivery
- Business Acumen – Ties product decisions to revenue, margin, and cost levers
- Systems Thinking – Understands how data, models, optimization, and workflows connect end to end
- Data-Driven – Uses experimentation and rigorous measurement to prove value
- Influence & Communication – Aligns executives, field leaders, and engineers without direct authority
- Change Leadership – Builds trust in AI with frontline users; earns adoption rather than mandating it
Qualifications:
- 5+ years leading technology implementations, data-driven decision tools, or product management, with a proven track record of delivering results
- An execution leader fluent in technology: a working understanding of data and core AI/ML concepts sufficient to partner credibly with data scientists and engineers
- Track record owning a business case and measuring realized value after launch
- Comfortable with KPI design, baselines, and test-and-learn measurement (e.g., pilots, A/B tests)
- Experience in field service, logistics, home services, or other multi-site operational businesses strongly preferred
- Experience leading adoption and change management for new tools or processes with frontline or operational teams
- Proven cross-functional leadership with operations, finance, marketing, IT, and external implementation partners
- Familiarity with Microsoft Fabric, Azure, Power BI, field service management platforms, or similar a plus
- Exposure to generative or agentic AI tools and AI governance a plus
- Comfortable working in Agile, PMO-led, or hybrid environments
- Bachelor’s degree in business, engineering, computer science, or a related field, or equivalent experience leading technology implementations
Additional Requirements:
- Ability to lead multiple concurrent, high-visibility programs in a fast-paced environment
- Executive presence; effective communication across technical and non-technical audiences, from front-line employees to the C-suite
- Strong organization, attention to detail, and follow-through
- Ability to travel as needed (approximately 1–2 times per quarter)
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At ARS/Rescue Rooter, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
ARS/Rescue Rooter AI Hiring
ARS/Rescue Rooter has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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