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Job Context
AI Sales Executive (Voice CSR) — The Most Important Seat We’re Filling This Year
Read this twice, because we don’t write job postings like this often.
Many businesses in our customers’ world run on tight profit margins — on average around 5%.
We’ve built the AI back-office solution to change that, helping customers work toward margins several times higher than they’re used to. Sit with that for a second. We are not asking you to squeeze more money out of customers — we’re asking you to help our customers become significantly more profitable than they’ve ever been. That’s the job. That’s the mission.
This is not order-taking. This is providing real business solutions for our trades and field-service management customers that are grinding on thin margins, showing them what AI can do to their
bottom line. You’ll be the person who introduces a transformation most of these businesses don’t even know is possible — and the better you are at it, the more companies you help change.What You’ll Do
What you'll own
You'll drive adoption of Simpro Group's AI back-office solutions across our existing customer base. This is a closing role — not a prospecting grind. The majority of your pipeline comes to you warm, handed off by Account Managers or triggered through marketing. Your job is to run tight, high-impact demos, connect our AI products to where a customer is losing margin today, and close.
You'll also self-generate 30% of your pipeline through your own outbound — but exclusively into our existing customer base. No cold outreach, no net-new logos. Just deeper penetration into accounts that already know and use Simpro.
What you will sell
You will be selling a revolutionary, AI-powered Voice CSR (Customer Service Representative) built exclusively for the trade and field service industries. Our technology integrates natively with Simpro, the leading global software that plumbing, electrical, HVAC, and fire protection contractors use to run their entire operations.Field service businesses lose thousands of dollars weekly because roughly 45% of inbound client calls go unanswered during busy hours or after-the-clock. Our hyper-realistic AI voice assistant solves this by answering calls 24/7. It qualifies leads, parses complex job types, matches appointments against real-time Simpro staff calendars, and books service jobs directly into their Simpro workflow on autopilot with zero manual data entry required
Day to day, you will:
- Receive warm inbound leads from the Account Management team and marketing — 70% of your pipeline is already primed before you pick up the phone.
- Run 3–4 demos per day with existing customers, moving deals through a short 1–2 call cycle from first conversation to closed.
- Connect our AI products directly to each customer's business — translating technology into the number they care about most: profit.
- Generate your own pipeline through self-sourced outbound into existing accounts, owning 30% of your book independently.
- Handle technical questions and objections with confidence — you'll be the product authority in every conversation.
- Build real relationships with trade business owners throughout the sales process, earning the trust that turns a demo into a done deal.
- Close consistently and at volume — this role is built for someone who wants multiple wins per week.
Who we want
Someone who wants to close, not just converse. You thrive in a fast-paced, high-volume environment where the deals are real, the cycle is short, and the wins stack up quickly. You have the technical credibility to earn a customer's respect in the first five minutes and the drive to turn that respect into a signature.
The base is strong, the commission is uncapped, and the pipeline is real. If you have a proven track record of closing in a short sales cycle, apply here.
What You’ll Bring
Our Core Values
We Are One Team
We Are Customer Centric
We Are Growth Minded
We Are Accountable
We Celebrate Success
Simpro, AroFlo, BigChange & ClockShark are equal opportunity employers with a best-of-class onboarding program and supportive team environments. This means that we want everyone to feel welcome with us and to provide equal opportunities for everyone, regardless of age, disability, gender reassignment, marriage and civil partnership, pregnancy and maternity, race, religion or belief, sex or sexual orientation, or any other non-performance factor.
If you'd like to join a fun and progressive organization, where there are opportunities to develop your career, please apply now with your CV/resume.
- Please note, no agencies will be accepted in the recruitment of this role.
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 Simpro, 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. Mid-level AI roles across all categories have a median of $193,200.
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.
Simpro AI Hiring
Simpro has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Miami, FL, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 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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