Interested in this AI/ML Engineer role at Housecall Pro?
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Why Housecall Pro?
Help us build solutions that build better lives. At Housecall Pro, we show up to work every day to make a difference for real people: the home service professionals that support America's 100 million homes. We're all about the Pro, and dedicate our days to helping them streamline operations, scale their businesses, and—ultimately—save time so they can be with their families and live well.
While our headquarters are based in Denver, Colorado, our team spans the globe, with teammates across Brazil, Poland, the Philippines, Mexico, and beyond. We care deeply about our customers and foster a culture where our company, people and Pros grow and succeed together—no matter where they're based. Leadership is as focused on growing team members' careers as they expect their teams to be on creating solutions for Pros.
We also offer:
- A generous benefits program that supports the whole you with medical, dental, vision, life, disability, and 401(k)
- Paid holidays and flexible, take-it-as-you-need-it paid time off
- Equity in a rapidly growing startup backed by top-tier VCs
- Monthly tech reimbursements
- A culture built on innovation that values big ideas, no matter where they come from
Role Overview
As a Senior Product Marketing Manager, you guide business owners through the diverse world of our products in a clear and engaging way, helping them realize their full potential in expanding their businesses. You excel at understanding their needs and pointing them to the right tools and solutions. You combine analytics and user insights to develop and optimize acquisition and customer marketing strategies to drive revenue. You are passionate about finding solutions for our service professionals (our Pros) and building delightful, on-brand experiences. You thrive in cross-functional environments, working with marketing, product, business operations, customer success, strategy & operations, and sales teams to perform customer research, drive product and commercial strategy through cross-functional influence, collect requirements, and manage asset production. You feel at home using data and AI tools to make decisions, whether through analytics, user research, customer feedback, or sales team feedback.
Our team is passionate, empathetic, hard working, and above all else focused on improving the lives of our service professionals (our Pros). Our success is their success.
What you do each day:
- Own the AI marketing strategy for our product line, shaping market perception and storytelling for AI features and capabilities
- Harness customer insights and competitive analysis to collaborate with product and marketing teams and develop effective product marketing strategies
- Monitor the competitive landscape and broader AI market on an ongoing basis
- Take high-level ideas and open-ended areas of opportunity and translate them into clear, actionable plans to execute against
- Design and run A/B tests and experiments to validate before scaling
- Serve as an example and mentor to junior team members
- Plan and coordinate go-to-market strategies for new AI product launches, ensuring a unified approach to launching new AI products
- Orchestrate product launch activities with cross-functional teams to maximize product uptake and visibility
- Monitor product performance throughout and after launch, refining our approach based on real-world feedback, market shifts, and new opportunities to sustain product momentum
- Empower Sales and Customer Success teams with essential tools and narratives, enabling them to communicate the product's story compellingly and effectively
Qualifications:
- 5-7 years of experience in Marketing, Strategy, or a related field
- Bachelor's degree in Marketing, Business Administration, related field, or equivalent work experience
- Direct experience marketing AI features or AI-powered products
- Demonstrated strategic experience beyond execution
- Experience planning and executing integrated go-to-market campaigns across multiple marketing channels
- Experience leading go-to-market strategies, approaches, and levers
- Proven track record of collaborating cross-functionally to achieve shared key performance indicators (KPIs)
- Proven experience driving team efficiency or output quality through the strategic use of AI tools and automation
What will help you succeed:
- A multi-dimensional background with a breadth of cross-functional interests or depth of specialized mastery
- Personal resilience to learn from scratch, and willingness to dive into ambiguous problems to formulate opinions and clear strategies
- Interest in AI functionality, use cases, and learning about the latest trends and launches on a daily basis
- Strong ability to translate technical complexity into simple, compelling messaging that drives action
- Experience working across both web and mobile platforms
- Empathy for the customer and a strong understanding of customer needs
- Creative problem-solver, motivated to test and learn
- Excellent communication and cross-functional influence skills
Location Dependent Information
This role is open to candidates and the expected salary range for this role is $128,000-$159,000 + Equity. The specific salary for the successful candidate will be determined based on permissible, non-discriminatory factors such as skills, experience, and geographic location. This role is also eligible to participate in Housecall Pro's equity plan and the following benefits: health care insurance (medical, dental, vision, disability), employee assistance program, 401(K), flexible time off, paid parental leave, tech reimbursement, and other company benefits. Housecall Pro is growing fast and we're scaling our team to help enable and accelerate our growth.
Founded in 2013, Housecall Pro helps home service professionals (Pros) streamline every aspect of their business. With easy-to-use tools for scheduling, dispatching, payments, and more, Housecall Pro enables Pros to save time, grow profitably, and provide best-in-class service.
Housecall Pro's brand portfolio includes Business Coaching by Housecall Pro, a business coaching solution for home services businesses. Our brands are united by a singular mission to champion our Pros to success.
We support more than 50,000 businesses and have over 2,000 ambitious, mission-driven, genuinely fun-loving teammates across the globe. If you want to do work that impacts real people, supported by a team that will invest in you every step of the way, we'd love to hear from you.
Housecall Pro celebrates diversity and we are committed to creating an inclusive environment. We are an equal opportunity employer and do not discriminate on the basis of gender, race, religion, national origin, ethnicity, disability, gender identity/expression, sexual orientation, veteran or military status, or any other category protected under the law. #LI-Remote
Privacy Notice for California Job Candidates - Housecall Pro
Use of Artificial Intelligence in Hiring and Engagement Processes: we may use artificial intelligence (AI)-powered tools to support the review and evaluation of employment and independent contractor applications, including screening and qualification assessments. All final hiring and engagement decisions are made by human representatives, not AI tools.
Any specific metrics, targets, or performance goals referenced in this posting are illustrative only and are subject to change based on evolving business needs.
Salary Context
This $128K-$159K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Housecall Pro, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($143K) sits 32% below the category median. Disclosed range: $128K to $159K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Housecall Pro AI Hiring
Housecall Pro has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Denver, CO, US. Compensation range: $159K - $159K.
Location Context
AI roles in Denver pay a median of $197,900 across 126 tracked positions. That's 7% below the national 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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