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About This Role
DemandTec is the Commercial Trade Intelligence platform built for the retail industry. We connect pricing, promotions, markdowns, and trade funds in a single demand science platform, giving retailers and CPG brands the connected intelligence to drive margin, grow commercial relationships, and win at shelf. With 25\+ years of retail expertise, a network of 7,800\+ CPG suppliers, and 120\+ retail banners on the platform, we provide the category depth and network scale that no point solution can match. Backed by Longshore Capital, we are in an accelerated growth phase, launching new capabilities, expanding globally, and building the team to match our ambition.
The AI Content Specialist owns DemandTec's content output across channels: our owned newsletter, social media presence, thought leadership, and CPG network engagement content. This role is designed from the ground up for someone who uses AI tools as a core part of their production workflow, not an occasional shortcut. Strong editorial judgment combined with genuine AI fluency is what makes this role high\-leverage and high\-impact.
You will write, produce, and publish content that builds category authority, grows our owned audience, and supports demand generation programs, working closely with the PMM and SVP on editorial direction.
Requirements
AI\-Native Content Production
- Own the end\-to\-end content production process using AI tools (such as Claude, Jasper, or equivalents) to draft, edit, and publish content at scale across formats and channels.
- Develop and maintain prompt frameworks and AI\-assisted workflows that ensure consistent brand voice and editorial quality across all content output.
- Own and maintain the content calendar across channels and audiences, hitting publishing cadences reliably without sacrificing quality.
The CTI Brief Newsletter
- Own DemandTec's flagship newsletter, The CTI Brief, from editorial direction through copy to send, building it into a must\-read for retail and CPG trade professionals.
- Develop editorial themes aligned to DemandTec's category positioning and thought leadership priorities.
- Track and optimize performance: open rates, click\-through rates, subscriber growth, and audience quality.
Social Media
- Own DemandTec's LinkedIn presence including company page content, post cadence, and executive social support for key leaders.
- Build and execute a social content calendar that reinforces category ownership, surfaces proof points, and drives meaningful engagement with our retail and CPG audience.
Thought Leadership and Campaign Content
- Draft blog posts, articles, and POV content in partnership with the PMM and SVP, supporting category authority and demand generation campaigns.
- Develop content supporting CPG network engagement programs targeted to the 7,800\+ supplier community on the platform.
- Partner with the Brand and Events marketer to ensure content supports event campaigns, trade show presence, and sponsored placements.
Qualifications
- 1 to 3 years of content experience in B2B technology, SaaS, or a related field.
- Genuine fluency with AI content tools: you use them daily, you know their limits, and you apply your own editorial quality control.
- Strong writing fundamentals: clear, audience\-aware copy with a track record of producing content that earns attention, not just fills a calendar.
- Experience contributing to a B2B social media presence with measurable results.
- Organized and self\-managing: you can own a multi\-channel content calendar with minimal oversight.
Preferred
- Experience writing for retail, CPG, supply chain, or enterprise software audiences.
- Newsletter or email marketing experience with ownership of performance metrics.
- Familiarity with HubSpot or similar tools for content distribution and tracking.
Equal Opportunity and Work Authorization
DemandTec is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, or any other characteristic protected by applicable law. Candidates must be authorized to work in the United States without employer sponsorship, now or in the future. This position does not offer visa sponsorship.
Benefits
Compensation and Benefits
Base salary: $50,000 to $60,000, commensurate with experience. DemandTec offers a comprehensive benefits package including health, dental, and vision insurance, 401(k), paid time off, and professional development support.
Salary Context
This $50K-$60K range is in the lower quartile 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 DemandTec, 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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($55K) sits 70% below the category median. Disclosed range: $50K to $60K.
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.
DemandTec AI Hiring
DemandTec has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $60K - $60K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% 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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