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About This Role
Location Zip Code:
84604Job Family:
Software Engineering We are seeking a hard-working, innovative, detail-oriented and creative team player to join our Aptive team! This is a full-time Director, Artificial Intelligence position located in Provo, Utah. Aptive has grown from a 2015 startup into one of the fastest-growing pest control companies in the US, serving 76 markets with 2,600+ employees. Our Pest Intelligence Center already blends AI, technician field observations, customer service data, and environmental modeling to forecast pest activity — and we're looking for the leader who takes that from a promising capability to a durable competitive advantage.
This is a new role and a builder's role. You'll define Aptive's AI strategy and then personally help ship it: models that decide how we route technicians, when we treat a property, which customers are at risk, and how our service teams spend their time. Success is measured in operational outcomes — retention, route density, first-time resolution — not in papers or proofs of concept.
What we offer:
- $200k Salary
- Annual Merit bonus of up to 20%
- Group Health, Dental, and Vision plans
- Pet insurance, Life insurance, and EAP benefits
- 401K with employer match up to 4%
- Paid holidays and paid time off
- Opportunity for advancement
- Upbeat and exciting company culture and much more!
Responsibilities include:
- AI strategy and roadmap for the Technology organization, prioritized by business impact and sequenced against real data and platform readiness
- The Pest Intelligence Center — evolving pest activity forecasting into proactive service planning across all markets
- Applied ML in production: demand and pest-pressure forecasting, route and scheduling optimization, churn and retention modeling, dynamic pricing inputs, and lead qualification for our sales channels
- Customer and technician-facing AI: contact center automation, agent assist, and decision support in the technician mobile experience
- Team building — hiring and growing a team of ML and data scientists, and setting the standards, tooling, and review practices they work to
- Partnership with Data Engineering on the warehouse and feature infrastructure these models depend on
- AI governance: model monitoring, drift detection, bias review, data privacy, and a clear position on responsible use of customer data
What the first year looks like
- Days 1–90: Audit existing AI and data assets, including the Pest Intelligence Center. Deliver a prioritized roadmap with named business owners and defined success metrics
- Months 3–6: Ship or measurably improve two production models tied to P&L outcomes. Stand up model monitoring and a deployment path
- Months 6–12: Hire the core team. Establish the intake process by which the business requests AI work, and demonstrate compounding returns on at least one flagship initiative
Requirements:
- 8+ years in data science, machine learning, or AI, including 3+ years leading teams
- A track record of ML systems in production that changed a business metric — you can explain the metric, the lift, and how you measured it
- Depth in forecasting, optimization, or recommendation systems
- Strong Python and SQL; fluency with a modern cloud data stack (we run Snowflake)
- Experience with LLM-based applications, and the judgment to know when they're the wrong tool
- Ability to translate between executives and engineers, and to say no to low-value AI requests
- Hands-on enough to review a colleague's model and debug a pipeline
Nice to have:
- Field service, logistics, home services, route-based operations, or seasonal-demand businesses
- Geospatial or environmental modeling
- Built a data science function from scratch at a mid-size company
- MS or PhD in a quantitative field
Aptive Intelligent Pest Control™ is redefining the industry with a smarter, more proactive approach to pest control. By analyzing millions of pest data points across neighborhoods, pest activity, and weather patterns, Aptive tailors treatment plans to help homeowners stay one step ahead. We geek out so our customers don’t freak out. You never thought bugs could be this cool.
Our culture is built on accountability, discipline, and high integrity—we work hard and have fun doing it. Aptive has been recognized for both its impact and workplace culture, including Inc. Magazine’s Best in Business and Best of State, Glassdoor’s Top 100 U.S. Workplaces, and Entrepreneur Magazine’s Best Companies in America. And we’re just getting started.
To learn more, visit www.aptivepestcontrol.com or follow Aptive on Facebook (@AptivePestControl), Instagram (@Aptive.PestControl), and LinkedIn (@AptiveEnvironmental).
- Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties, or responsibilities that are required of the employee for this job. Duties, responsibilities, and activities may change at any time with or without notice. If you require reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please inform your recruiter.
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 Aptive Environmental, 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. Director-level AI roles across all categories have a median of $272,275.
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.
Aptive Environmental AI Hiring
Aptive Environmental has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Provo, UT, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 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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