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About This Role
About LawnStarter
LawnStarter is the nation's leading on demand marketplace for lawn care and outdoor services, with over $150M in annual bookings. We are expanding beyond lawn care to become the one stop shop for all home services. Becoming the most discovered, cited, and trusted brand in our category is how we get there.
About the Growth Team
Growth owns the full acquisition funnel across our brand portfolio. Some of the most recognized names in outdoor services, reaching millions of homeowners every year. SEO and organic discovery are foundational to how customers find us, and this role leads the team responsible for making that happen at scale. You will report directly to the CMO and operate at the intersection of content, technical SEO, product, and the emerging world of AI powered search.
The Challenge
The game changed. Rankings are no longer the prize. Recommendation is. When a homeowner asks ChatGPT, Claude, Perplexity or Google AI who to hire, our job is to be the answer. Not page one. The answer.
That takes two bets running together. First, brand presence across every surface where homeowners and machines form trust. PR, journalists, creators, community platforms, review sites, Reddit, Nextdoor, partner ecosystems. Authority comes from the network, not from the page. Second, exposing first party data nobody else can publish. Pricing patterns. Completion times. Regional demand. Marketplace outcomes. That is the kind of original, expert evidence AI systems reward and that commodity SEO can no longer produce.
We have a talented team and strong brand equity. What we need is the leader who already operates this way and has the results to prove it. Not one we have to convince.
The Role
You will own organic search and AI discovery across our brand portfolio. You set the vision, the roadmap, and the standards for how we compete in a world where Google, ChatGPT, Perplexity, and AI Mode are all becoming primary discovery surfaces.
The team is small by design. One SEO analyst. A lean research and editorial group that produces data driven studies and guides. A no code publishing platform that gives you direct control of thousands of pages. Several strategic functions sit intentionally unowned and waiting for you to define. AI visibility tracking. Editorial direction. Content pruning. Expert sourcing strategy. You decide what to keep, what to retire, and what to build. We are scaling leverage, not headcount.
This is a strategic, high visibility role. You will shape company wide priorities, partner with engineering, data, product, design, and executive leadership, and be the internal authority on where organic discovery is heading and what it takes to win.
What makes this role different.
- You are building a new playbook, not optimizing an old one. The opportunity requires fresh thinking about how technical SEO, content, structured data, and AI visibility work together in 2026 and beyond.
- Multiple major brands, millions of users, real revenue stakes. Organic drives a meaningful share of our business. The decisions you make here compound across our entire brand portfolio at scale.
- AI Search is the frontier. Google AI Overviews, ChatGPT, Perplexity. You are not just optimizing for blue links anymore. This role requires genuine expertise in how LLMs and answer engines discover, evaluate, and cite content.
- You have leverage. A small internal core, a no code publishing platform that gives you direct control of thousands of city, near\-me, service, and informational pages (including schema markup and meta data), and first party data nobody else in the category can publish. No engineering dependency. No sprint queues. Move at the speed of a hypothesis. Direct what is here. Build what is missing. Decide what to add.
What You'll Own
- Organic strategy across our brand portfolio. The vision, roadmap, and standards for how our brands compete in organic search and AI powered discovery.
- AI Search visibility. Establishing our brands as cited, authoritative sources in Google AI Overviews, ChatGPT, Perplexity, and emerging conversational search surfaces. Through answer first content architecture, entity authority, and structured data that AI systems can discover, extract, and trust.
- Technical SEO and architecture. Large scale technical infrastructure, Core Web Vitals, structured data, crawl efficiency, and the product architecture decisions that drive organic performance.
- Content strategy and production. Directing the editorial work that produces the pages, guides, studies, and resources that rank, get cited, and convert.
- Team leadership and hiring. Assessing the current core, making the talent calls that need to be made, and building the team you need for the new direction.
- Cross functional partnership. Working closely with Engineering, Analytics, and Data to build the technical and measurement foundation organic needs to operate at scale.
Problems to Solve
Recovering and growing traffic at scale One of our brands has seen significant traffic decline. Rankings that used to sit in positions 3 to 5 have slipped to page 2\. Diagnosing the root cause is the first order of business. Whether the driver is algorithm shifts, content quality, technical debt, AI Search cannibalization, or some combination, the recovery plan needs to address it. The answer will not be simple, and it will not be found by outsourcing it to a framework.
Winning in AI powered discovery The rules of organic visibility are being rewritten in real time. Google AI Overviews, ChatGPT, and Perplexity are becoming primary discovery surfaces for the queries that matter most to us. Most brands are still figuring out how to show up in these environments. How do you build a structured, repeatable approach to AI Search visibility and turn it into a competitive advantage before others catch up?
Going from defense to offense For the last several years it has felt like we have been playing defense as the landscape shifted around us. The old playbook worked until it didn't. This role flips the posture. Go back on offense. Dominate the next era of organic discovery across more verticals than before. How do you go from reacting to leading in the first 90 days?
Defining how to measure AI Search Traditional SEO has clear signals. Rankings, impressions, clicks. AI Search visibility is murkier, but the signals are there if you know where to look. Log data, crawler activity, and emerging tooling can surface a lot, but not all are wired up yet. How do you build a measurement approach that turns raw signals into a reliable view of AI Search visibility, one you can actually use to make decisions and demonstrate progress?
Requirements Who You Are
Sees around corners. You have been building for where search is going, not where it has been. You followed the AI search shift before it was conventional wisdom and you can articulate, specifically, what it means for a business like ours. This is unlikely to be a good fit if you are still primarily optimizing for the 2020 version of SEO or treat AI search as a future concern.
Understands external visibility as foundational. You recognize that in the AI era, authority comes from being repeatedly cited and mentioned across external networks. Journalists, creators, review platforms, industry directories, communities. You have built external earned media strategies and understand how to navigate those ecosystems. You see this as core SEO strategy, not bolt on marketing work. This is unlikely to be a good fit if you view SEO as primarily on site optimization or if you see earned media and PR as separate from organic search strategy.
Understands organic as a revenue driver, not just a traffic channel. You operate with a business mindset. You tie organic initiatives to CAC, acquisition, and revenue impact, not just impressions and clicks. You can speak the language of payback periods, cohort economics, and margin contribution. Organic is foundational to how we grow, and you see your job as compounding revenue value across the entire brand portfolio, not maximizing traffic at any cost. This is unlikely to be a good fit if you are satisfied with organic metrics that don't connect to downstream business outcomes.
Diagnoses fast, acts faster. You use data to find the signal in the noise, make a call, and move. You are not the person who needs six more weeks of research before forming a hypothesis. You know the difference between analysis that unblocks action and analysis that substitutes for it. This is unlikely to be a good fit if you are known for thorough research that rarely translates into a clear recommendation.
Builds and elevates teams. You have led at scale before and made the hard talent calls when they were needed. You are also comfortable starting small and building the team you need around you. You develop leaders, not just contributors, and you create an environment where people know what great looks like. This is unlikely to be a good fit if you prefer to inherit a big team rather than build one.
A company wide evangelist. You don't just own SEO. You sell the vision for it. You can explain the AI Search opportunity to a CFO as clearly as you can explain it to a technical SEO. You influence roadmaps, secure investment, and get cross functional partners excited about organic. This is unlikely to be a good fit if you prefer operating within your lane and leaving the advocacy to others.
Technically fluent and willing to ship. You can get into the weeds on structured data, crawl budgets, and Core Web Vitals, and you know when to. You also know when to step back and set direction. With a small team, the line between executing and leading blurs. You are comfortable on both sides of it. This is unlikely to be a good fit if you need a big team underneath you to do the work.
AI native. You use AI tools daily. For research, content, analysis, and workflow acceleration. More importantly, you understand how LLMs and generative AI work well enough to build an AI Search strategy grounded in how these systems actually discover and evaluate content. This is unlikely to be a good fit if your AI knowledge is surface level or limited to using ChatGPT for drafts.
This Role Is NOT
- A pure IC role. You will execute alongside the team and roll up your sleeves on the work that needs doing. With a small core and several functions unowned, you cannot delegate everything from day one. But you also own the direction, the hiring, and the standards. If you only want to ship the work yourself and report up, this is too broad. If you only want to set strategy and never touch the work, this is too in the weeds.
- A single brand role. You own SEO and AI Search across our entire brand portfolio. Every brand matters. If you would prefer to go deep on one and treat the others as secondary, this isn't the right fit.
- A research and recommend role. We don't need more analysis of what competitors are doing. We need a clear point of view on where we are going and the execution to get there. If you are most comfortable staying in research mode, this role will frustrate you.
- A role waiting on engineering. Our no code web publishing platform migration puts production pages directly in marketing's hands. Thousands of organic city and near\-me pages, partner pages, all of it. If slow execution has been your constraint before, it won't be here. What you will need is the judgment to use that speed well.
Benefits
- Base salary. $170,000 to $190,000 USD.
- Equity. Organic discovery is foundational to how we grow. The value you create compounds across our entire brand portfolio and every channel downstream. We want you invested in that long term outcome.
- Healthcare. Medical, dental, and vision.
- Fully remote. Work from anywhere in the US.
- Flexible PTO. Focused on outcomes.
*LawnStarter provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, or genetics. We comply with applicable state and local laws governing nondiscrimination in employment.*
Salary Context
This $170K-$190K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At LawnStarter, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. Disclosed range: $170K to $190K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
LawnStarter AI Hiring
LawnStarter has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $190K - $190K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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