Interested in this AI/ML Engineer role at TNT Growth?
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About This Role
About Us
We are TNT Growth – a performance\-driven agency that helps leading companies acquire more leads and customers. Our core focus is on Paid Search management, conversion rate optimization, and reporting \& analytics. We are sharp, collaborative, and results\-obsessed. Our clients include We Level Up, Salesforce, Gusto, Formula 1 Miami, and Sweet James.
The Opportunity
We’re hiring a Senior Growth Manager with specialized experience in rehab clients to lead our performance marketing strategies in the addiction treatment space. This is a hands\-on role where you will directly influence client success by scaling paid media performance and driving measurable business outcomes. The ideal candidate will have a strong background in digital marketing, particularly in rehab or healthcare\-related industries, and be familiar with the unique challenges and regulations in this space.
What You’ll Do
- Client Strategy and Execution: Own the strategy, execution, and results for multiple rehab clients, with a particular focus on Paid Search and potential expansion into other platforms like Meta Ads.
- Admissions Optimization: Partner with the client's admissions team to ensure they are following best practices, using the right tools, inputting data correctly, and operating efficiently. Identify and address bottlenecks that impact lead\-to\-admit performance, ensuring that strong marketing results aren’t lost due to operational gaps.
- Campaign Management: Launch, optimize, and iterate campaigns quickly based on performance data, driving growth for rehab centers.
- Funnel Optimization: Lead full\-funnel strategies that drive patient acquisition, from initial traffic sources to conversion, ensuring continuous improvements in CPA (Cost per Acquisition) and ROAS (Return on Ad Spend).
- Collaboration: Work cross\-functionally with analysts, creative strategists, and designers to refine ad creatives and landing page experiences to ensure maximum impact for rehab clients.
- Client Relationship Management: Serve as the day\-to\-day contact for 10\-15 clients, providing timely updates, insights, and strategic recommendations. Build and maintain strong relationships with clients by understanding their needs and driving real business results.
- Compliance: Ensure campaigns adhere to industry regulations, including HIPAA, ADA, and LegitScript certifications, while managing patient privacy and adhering to ethical standards.
- Proactive Problem\-Solving: Quickly address underperformance and collaborate with internal teams to develop solutions. Track and report on key performance metrics regularly.
What Success Looks Like
- Exceeding KPIs: Achieve client KPIs, including strong Cost/VOB and Cost/Admit numbers.
- Optimized Testing: Strong testing velocity with continuous data\-backed iterations that lead to optimized performance.
- Client Trust: Become a trusted advisor to clients, consistently demonstrating expertise in the addiction treatment space.
- Team Trust: Deliver seamless execution with cross\-functional teams, ensuring all aspects of campaigns are executed without oversight.
What Makes You a Fit
- Experience: 4\+ years of hands\-on paid media experience, with a proven track record of working with rehab centers
- Analytical: You have an analytical mindset and can interpret data to drive decisions and strategic adjustments.
- Adaptability: You are comfortable working in a fast\-paced, evolving environment and know how to pivot when things change quickly.
- Communication: Strong written and verbal communication skills. You present confidently and know how to manage client expectations.
- Attention to Detail: You’re meticulous with your work – you double\-check everything, ensuring flawless execution.
Bonus Points
- Industry\-Specific Expertise: Familiarity with the rehab industry, its challenges, treatment approaches, and unique patient acquisition strategies.
- Platform Proficiency: Experience with Google Ads and Microsoft Ads, as well as any rehab\-specific marketing tools (e.g., call tracking and verification systems).
Why Join Us?
At TNT Growth, we value results\-driven, innovative thinkers. If you’re passionate about performance marketing and want to make a measurable impact in a growing industry, we want you to be a part of our team.
Compensation:
$100k\-$150k
Fully Remote
Flexible PTO
401k
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Salary Context
This $100K-$150K range is above 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 TNT Growth, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($125K) sits 25% below the category median. Disclosed range: $100K to $150K.
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.
TNT Growth AI Hiring
TNT Growth has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $100K - $150K.
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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