Interested in this AI/ML Engineer role at Halstead Media Group LLC?
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About This Role
Work from anywhere while running high\-impact ad campaigns that drive real leads for lawn, landscape, and outdoor living companies. As a Halstead Media Paid Media Manager, you’ll own performance across Meta and/or Google Ads—building, optimizing, and scaling campaigns that keep clients growing and teams proud of the results. What makes this opportunity unique is the mix of measurable impact and perks that support your life and career: remote flexibility, paid training and professional development, snack/coffee/tea subscriptions, premium medical (100% company\-paid for employee), dental (50% company\-paid) with optional vision, 15 PTO (includes vacation and sick) days to start plus paid holidays, 401(k) with match, and branded company swag.
What You’ll Do
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This isn’t a “set it and forget it” role—it’s a hands\-on performance position where your strategic thinking and execution directly shape client outcomes. You’ll go beyond basic management to become the paid media owner who turns goals into lead volume, better quality, and consistent delivery—while collaborating with creative, web, and client\-facing teams to remove friction and improve conversion.
### Campaign Ownership \& Performance Strategy
- Own lead generation performance for clients across Meta (Facebook/Instagram) and/or Google Ads (search and beyond as applicable).
- Set up and launch campaigns (cold, retargeting, events, video, etc.) with clear structure, targeting, and tracking.
- Use KPIs like leads/conversions as the main measure of success, supported by metrics like CTR, quality score, and cost efficiency.
- Identify performance issues early, diagnose root causes, and build a clear plan to recover results—without waiting for someone to ask.
- Stay current on platform changes and apply updates thoughtfully to improve outcomes.
- High possibility of also working on Microsoft Ads, Linkedin Ads, and other paid media platforms.
### Optimization, Budget Control \& Execution
- Monitor and optimize active campaigns regularly: audiences, creative, copy, bidding, and campaign type selection.
- Manage budgets responsibly—avoiding chronic under\- or overspend while keeping momentum strong.
- Write compelling, relevant ad copy that matches the service, audience, and intent (and is clean, accurate, and typo\-free).
- Build ad creatives in Canva, and partner with designers when higher\-level creative is needed.
- Document key changes, learnings, and repeatable wins inside the company’s training/process platform.
### Cross\-Functional Collaboration \& Conversion Improvements
- Collaborate with project managers, leadership, and creative teams to align campaigns to strategy and timelines.
- Provide clear recommendations that improve conversion—such as landing page updates, offer clarity, tracking improvements, and funnel fixes.
- Support reporting and updates by translating performance data into what matters: what happened, why it happened, and what you’re doing next.
- Participate in Zoom sessions for collaboration, training, workshops, and troubleshooting—especially around tracking forms/calls.
You’ll Thrive in This Role If You:
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- Enjoy owning outcomes and taking pride in performance that’s easy to measure.
- Communicate clearly and proactively—especially when performance is down and action is needed.
- Are organized and comfortable juggling multiple accounts, priorities, and deadlines.
- Think strategically, but move fast (you don’t get stuck overthinking).
- Like learning—because the work touches tracking, landing pages, creative strategy, and more.
What Success Looks Like
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- Consistent lead generation results clients can feel—and explain inside their business.
- Clean, accurate builds: strong copy, correct settings, thoughtful targeting, and well\-structured campaigns.
- Budgets managed responsibly with steady optimization (not fire drills).
- Cross\-team trust: you bring clarity, ideas, and follow\-through that makes everyone better.
- Visible alignment with our values: dependable, curious, results\-driven, and action\-oriented.
Required Experience
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- Experience creating and managing Meta OR Google Ads campaigns (you do *not* need both).
- Strong written communication skills (ad copy quality matters here).
- Comfort working in modern tools (Slack, Zoom, HubSpot, Asana, reporting tools, etc.).
- Bonus: experience with conversion tracking, call/form tracking, landing page optimization, or LinkedIn Ads.
Additional Opportunities (Variety \+ Growth)
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To broaden your marketing skillset—and help the team during coverage— you may also contribute to initiatives like:
- Supporting reputation management updates
- Competitor research
- Call tracking setup support
- Internal projects that improve client satisfaction and processes
- Learning/supporting SEO depending on interest and strengths
About Halstead
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Halstead Media Group helps landscape and outdoor living companies eliminate instability, fuel growth, and build lasting legacies with specialized, year\-round marketing systems.
We’re a fast\-growing, performance\-driven team that takes ownership of results and builds real client relationships rooted in trust. Clients stay with Halstead because we deliver best\-in\-class ROI, proactive strategy, and a human connection that makes marketing feel clear—not chaotic. As a recognized leader in the green industry, we’re shaping what’s next through education, partnerships, and marketing that produces real outcomes.
Salary Context
This $52K-$68K range is below the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Halstead Media Group LLC, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($60K) sits 64% below the category median. Disclosed range: $52K to $68K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Halstead Media Group LLC AI Hiring
Halstead Media Group LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $68K - $68K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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