Interested in this AI/ML Engineer role at The Backyard Life?
Apply Now →Skills & Technologies
About This Role
About the Role
We're looking for an operations-minded AI Integrator to find the problems slowing our business down and solve them with AI and automation. This is not a research or model-building role. You'll map how work actually gets done, design and ship AI-powered workflows, measure what they deliver, and make sure the team adopts them and they keep working over time.
The ideal candidate pairs strong operational judgment with hands-on AI integration skills. You don't just prompt ChatGPT or Claude. You've built and deployed real workflows, integrations, and AI agents into live business processes, measured the results, and kept them running long after launch.
Core Responsibilities
- Map existing processes and identify the highest-ROI opportunities for AI and automation
- Design, build, test, and deploy AI-powered workflows, integrations, and agents
- Identify manual, repetitive work and replace it with automated or AI-assisted processes
- Connect business systems so data flows cleanly between operations, accounting, and reporting
- Monitor performance, quality, and cost of deployed systems; fix what breaks and improve what underperforms
- Build error handling, guardrails, and human review steps into anything that touches customers, money, or sensitive data
- Measure impact with clear metrics (time saved, errors reduced, revenue impact, adoption) and report results to leadership
- Document processes, systems, and standard operating procedures so they can be maintained and handed off
- Train team members on new systems and drive adoption
- Decide when an off-the-shelf tool fits and when custom work is worth it, and keep costs under control
- Evaluate new AI tools and recommend what's worth implementing
- Partner with ownership and leadership to prioritize problems and deliver measurable improvements
Systems You'll Work In
- Claude, ChatGPT, and other AI platforms
- Automation tools such as Zapier, Make, or n8n
- QuickBooks Online
- Jobber
- Notion
- Microsoft Excel / Google Sheets
Key Competencies
- Systems Thinker: You see how the pieces of a business connect and design workflows that hold up in real use, not just in a demo
- Outcome-Driven: You measure success in business results like time saved and errors avoided, not in tools deployed
- AI Builder: You've shipped working AI workflows and agents into production and you're always looking for the next thing to automate
- Quality-Minded: You plan for what happens when AI gets it wrong, and you build in testing, monitoring, and guardrails
- Cost-Aware: You know what your systems cost to run and you keep that in check
- Communicative: You explain technical systems in plain language and get non-technical teams to actually adopt new ways of working
- Proactive: You spot problems and inefficiencies before they become expensive and fix them without being asked
Requirements
- Background in operations, RevOps, project or program management, consulting, or automation
- Hands-on experience integrating AI into real business workflows, well beyond prompting a chatbot. Required.
- A track record of shipped, working AI workflows or agents with measurable before-and-after results. Required. Be ready to walk us through what you built, what broke, how you fixed it, and what it delivered.
- Comfort with no-code and low-code automation tools (Zapier, Make, n8n, or similar) and working with APIs
- Familiarity with LLMs, AI agents, and retrieval-based setups (connecting AI to company documents and data)
- Basic data skills and advanced spreadsheet proficiency
- Working knowledge of security, privacy, and cost controls for AI systems
- Proven ability to drive adoption of new systems with non-technical teams
- Experience with QuickBooks Online, Jobber, Notion, or field service software is a plus
- Experience in home services, contracting, or a related industry is a plus
The Short Version
You'll find the operational problems holding the business back and solve them with AI and automation. You bring sharp operational judgment, real experience shipping AI workflows and agents, and the discipline to measure results and keep systems reliable. If you'd rather turn messy processes into working systems than talk about AI, we want to talk to you.
Pay: $35.00 - $50.00 per hour
Work Location: In person
Salary Context
This $72K-$104K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At The Backyard Life, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($88K) sits 58% below the category median. Disclosed range: $72K to $104K.
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.
The Backyard Life AI Hiring
The Backyard Life has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Saint George, UT, US. Compensation range: $104K - $104K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.