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About This Role
This job is for you if...
You’ve been the person who gets handed the messy local SEO problem and actually figures it out.
A client is stuck around position 5 across their geo\-grid for their primary service. Their Google Business Profile looks fine at a glance. The service pages exist. The citations are mostly clean. The content is technically “optimized.” But the phone is not ringing enough, and nobody has a clear answer.
That is the kind of problem you like.
You know how to dig into the data, separate symptoms from causes, and decide what needs to happen next. You understand local search, traditional organic search, and the way AI search is changing discovery. You are not guessing based on whatever SEO thread you read last week. You have real experience, real opinions, and enough humility to keep testing when the data disagrees with you.
You are also comfortable talking to clients. This is not a role for someone who wants to hide behind audits and never explain their thinking. You’ll need to present strategy clearly, answer direct questions, and help business owners understand what we are doing, why it matters, and what should happen next.
What this role actually is
You will own SEO strategy for a portfolio of mostly local service businesses, with some national SEO clients mixed in.
Most of our clients care about calls, booked appointments, form fills, local visibility, and revenue. Rankings matter, but only because they are supposed to turn into business. Your job is to understand what is happening in search, decide what needs to change, and make sure the work gets implemented well enough to move the needle.
This is a senior strategy role with some hands\-on execution. Roughly:
- 20\-25% client meetings, presentations, and client\-facing follow\-up
- 10% internal meetings
- 55% strategy, analysis, planning, QA, and direction
- 15% hands\-on implementation
You will not be spending your week writing generic SEO content from scratch. AI has changed that. We need someone who knows how to use AI to accelerate research, briefs, analysis, content development, and QA, then apply human judgment to make the work accurate, useful, local, trustworthy, and aligned with the client’s business.
What you’ll work on
You’ll diagnose local SEO performance problems across Google Business Profiles, geo\-grids, organic rankings, technical SEO, site structure, content quality, reviews, authority, internal linking, conversion paths, and competitor movement.
You’ll build SEO strategies for local service businesses where service area, proximity, relevance, reputation, and trust all matter.
You’ll guide location pages, service pages, and geo\-targeted content in a way that is useful to users and does not drift into thin doorway\-page nonsense.
You’ll help clients adapt to AI search, AI Overviews, answer engines, LLM\-driven discovery, and the difference between being ranked, being cited, and being trusted.
You’ll use tools like GA4, Google Search Console, Google Business Profile, geo\-grid tracking platforms, Screaming Frog, Ahrefs, Semrush, BrightLocal or similar tools, WordPress, Looker Studio, and AI tools such as Claude, ChatGPT, Gemini, Perplexity, Claude Code/Codex\-style workflows, MCP\-enabled tools, and AI search visibility platforms.
You’ll create clear recommendations, assign or coordinate execution, review the work, and follow through until the client sees progress.
What makes someone good at this
You have deep local SEO experience. You understand how local service businesses win search visibility and how that differs from ecommerce, SaaS, publishing, or broad national content SEO.
You are already paying attention to GEO/AEO/AI search. You do not need to pretend the whole SEO world has been reinvented overnight, but you do need to understand how discovery is changing and how to help clients show up in both traditional results and AI\-assisted answers.
You are analytical and detailed. You can look at rankings, GBP data, Search Console, organic traffic, conversion data, page quality, competitors, and site structure, then come back with a practical plan.
You communicate like a grown\-up. Clients should leave a call feeling like you understand the problem and have a plan, not like they were buried under jargon.
You are comfortable with AI, but not impressed by it for its own sake. You know where it saves time, where it creates risk, and where human judgment still matters most.
You follow through. If you say you are going to investigate something, you investigate it. If a recommendation requires implementation, you make sure it does not die in a spreadsheet.
You should probably have
- 5\+ years of SEO experience, with significant hands\-on local SEO experience
- Experience working with local service businesses, home services, healthcare, legal, or similar local lead\-generation clients
- Strong understanding of Google Business Profile optimization, local ranking factors, geo\-grid analysis, reviews, local landing pages, technical SEO, schema, internal linking, and content strategy
- Experience presenting SEO strategy and performance to clients
- Comfort using AI tools as part of a real workflow, not just occasionally generating copy
- Strong QA instincts and attention to detail
- Agency experience or experience managing multiple SEO accounts at once
This is not a good fit if...
- You want a junior SEO execution role.
- You mostly want to write blog posts.
- You need someone else to tell you what the strategy is.
- You are uncomfortable being on client calls.
- You treat AI\-generated output as finished work.
- You think local SEO is just citations, keywords, and posting on GBP.
About Motivent Marketing Inc.
Motivent Marketing Inc. is a remote marketing agency serving mostly local service businesses, with some national clients as well. We care about practical marketing that creates real business outcomes: calls, leads, booked jobs, better visibility, and stronger trust with the people our clients are trying to reach.
We are building for how marketing works now. That means using AI intelligently, moving faster, keeping human judgment at the center, and hiring people who can think, communicate, and own outcomes.
Pay: $65,069\.44 \- $86,793\.30 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Flexible schedule
- Health insurance
- Health savings account
- Paid time off
- Parental leave
- Retirement plan
- Vision insurance
Work Location: Remote
Salary Context
This $65K-$86K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Building Brands Marketing, 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($75K) sits 58% below the category median. Disclosed range: $65K to $86K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Building Brands Marketing AI Hiring
Building Brands Marketing has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $86K - $86K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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