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About This Role
Manager, Marketing AI Solutions
Nashville Hybrid or Remote US
About the Role
As Manager, Marketing AI Solutions, you will build AI capabilities that expand Marketing’s reach and become part of how we do our jobs every day.
Working cross\-functionally across Creative, Web, Paid, Growth, Analytics, and Research, you’ll combine hands\-on development with practical problem\-solving to take high\-impact AI solutions from idea to production.
What You’ll Do
1\. Build AI Solutions
- Design, build, and deploy flagship AI agents, automations, and integrations for Marketing.
- Rapidly prototype AI tools, validate with users, and scale successful concepts to production.
- Connect marketing platforms to enterprise AI environments via APIs, MCP, and agent frameworks.
- Prioritize pragmatic architecture—using low\-code/out\-of\-the\-box tools over custom code when faster and easier to maintain.
- Continuously refine AI solutions based on user feedback, evolving tech, and business needs.
- Develop reusable code modules, APIs, and implementation patterns for internal builders.
2\. Collaborate \& Advise
- Partner with Marketing teams to evaluate high\-value AI use cases and technical requirements.
- Guide non\-technical teams in turning one\-off prompts into shared, reusable workflows.
- Evaluate feature requests to balance business impact, cost, and technical feasibility.
- Help teams resolve technical roadblocks, review designs, and secure production implementations.
- Translate complex technical tradeoffs into clear language for non\-technical stakeholders.
- Partner with Security and Tech teams to ensure solutions meet governance and compliance standards.
3\. Grow AI Capability
- Evaluate emerging AI tools, models, and practices to expand Marketing capabilities.
- Foster an internal builder culture through documentation, workshops, and best practices.
- Evolve AI development standards and guardrails alongside changing enterprise policies.
What We’re Looking For
Required Qualifications
- 3\+ years in solutions development, MarTech, DevEx, or technical automation.
- Experience with ML, computer vision, NLP, and predictive modeling.
- Proven track record building production\-ready AI agents, workflows, and LLM apps with human\-in\-the\-loop review.
- Launching self\-hosted and 3rd\-party AI solutions using modern, AI\-native developer workflows (e.g., Cursor, Claude Code, GitHub Copilot), Python strongly preferred.
- Model/voice customization using prompt engineering and RAG.
- API proficiency with REST, modern auth, and protocols like MCP.
- Integrating marketing platforms (Adobe, Figma) with generative tools (DALL\-E 3, Imagen, Veo3\) and workplace apps (Slack).
- Evaluating and testing AI solutions for reliability, security, and responsible use.
- Managing multiple cross\-functional projects in a fast\-paced, evolving AI landscape.
- Strong written, verbal, and interpersonal communication skills.
Nice to Have
- Certification in AI, machine learning, or data science.
- Building agentic workflows across core marketing domains (Creative, Digital, Analytics, Web, Paid, Email, Ops).
- Experience with SQL, vector databases, and marketing data pipelines.
- Fine\-tuning foundation models (via LoRA, SFT, or provider APIs).
- Familiarity with additional generative media APIs (image, video, audio).
Who Thrives Here
You value momentum over over\-engineering. You enjoy solving messy, ambiguous problems and using your development skills to turn ideas into practical solutions quickly, without adding unnecessary technical debt.
You’re an empathetic mentor. You’re equally comfortable diving into technical details and collaborating with non\-technical teammates. You naturally explain complex technical tradeoffs in everyday language and take pride in helping teammates overcome hurdles.
What Success Looks Like (First Year)
- Impact: New AI\-powered capabilities launched that expand what Marketing can accomplish, with production\-ready solutions actively adopted across core Marketing teams.
- Culture: Widespread adoption of established AI frameworks, enabling Marketing teams to autonomously build and maintain simple automations.
- Reputation: Trusted across Marketing to turn *"can we?"* into *"we did."*
- Metrics: Measurable reductions in manual effort, faster delivery of marketing solutions, and improved operational efficiency.
Why This Role?
Marketing is evolving how work gets done—from transforming creative workflows and accelerating testing to generating faster insights and enabling teams to accomplish more with AI. This role is a critical enabler of that modernization.
You’ll get to develop novel solutions, help shape how Marketing builds with AI, and grow AI capability across the department. Backed by leadership support and a growing AI ecosystem, you’ll have the autonomy to experiment, build, and turn ambitious ideas into practical solutions that teams rely on every day.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Asurion, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Asurion AI Hiring
Asurion has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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