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About This Role
Job Number: 102480 Updated on: 07/13/2026
Overview: A remote GTM marketing role turning cutting\-edge AI capabilities into stories that resonate with gamers and PC builders.
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Location: Remote in States Listed
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Compensation: $75,000 \- $83,000
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Liaison Creative \+ Marketing is hiring a full\-time, full\-benefits, remote AI GTM Marketing Specialist. This role sits at the center of go\-to\-market messaging, positioning, and content for a fast\-growing AI product portfolio at one of the most iconic names in computing and semiconductor technology. This role is fully remote but we can only consider candidates who reside in the following states in CST and EST time zones: CT, FL, GA, LA, MA, MD, MI, MN, NC, NV, TN, TX, UT, VA, WI with a strong preference for candidates located in Austin, TX. This isn't a role for someone who just knows about AI. We're looking for someone who lives it: running local models on their own machine, testing agentic workflows before anyone asks them to, and showing up with an opinion on what's actually good versus what's just loud on social media. If you can turn "here's what our silicon can do" into a story a gamer, a developer, or a Fortune 500 buyer actually cares about, and you can keep a room full of stakeholders aligned and moving toward a launch date, we want to talk to you.
For over 30 years, Liaison has pioneered the fully managed hybrid marketing team model, embedding dedicated creative and marketing talent directly within some of the world's most recognized brands. At Liaison, you are our people, not a contractor on a roster or a headcount line item. As a full\-time Liaison professional, you're embedded inside one of the most complex and exciting marketing organizations in the world, backed by the direct support of a small Austin agency with the benefits, support, and career development you deserve.
- Own the strategic point of view on AI value, working alongside a technical AI lead to turn strategy into activations, assets, and campaigns customers actually see
- Develop AI\-focused GTM messaging and positioning that clearly communicates the client's AI value proposition, from data center to desktop
- Translate technical AI capabilities into customer\-facing content, including blogs, solution briefs, product narratives, web copy, and campaign assets. Think less whitepaper, more "here's how to run this model on your own rig this weekend"
- Plan and execute holistic GTM activations for AI solutions, coordinating across product, sales, ecosystem, PR, analyst relations, and demand generation teams
- Own project management for GTM launches: build agendas, run meetings, and aggregate notes across teams to keep everyone aligned and moving forward
- Shape positioning around local AI and agentic workloads, highlighting how the client's technologies enable next\-generation AI experiences, on\-device, on your terms
- Partner with technical, product, and ecosystem teams to validate messaging for local AI and agentic AI use cases, translating platform capabilities into credible thought leadership and GTM content. You should be comfortable enough with the tech to catch a claim that doesn't hold up before it ships
- Support paid and organic social activation efforts that build awareness and drive demand
- Bring genuine curiosity to the table: try the new local model release, poke at the new agent framework, and show up with a point of view before the brief asks for one
### How to be a top candidate for this job:
- 3\+ years of experience in product marketing, GTM marketing, or content marketing, ideally supporting technology or AI\-related products
- In\-depth knowledge of AI: how models actually work, what they're good for, and the full scope of what agentic computing can do. You don't just cite AI trends, you've tested them yourself, starting with local\-first
- Demonstrated ability to translate complex technical concepts into clear, compelling narratives for varied audiences
- Experience coordinating cross\-functional go\-to\-market launches spanning product, sales, PR, analyst relations, and demand generation
- A programming or technical background, or at least a real interest in one, is a plus. You don't need to write production code, but you shouldn't be afraid of a terminal window
- Ready to move fast and get out of your comfort zone in a high\-velocity environment. You'll be working alongside engineers and technical leads, so knowing your way around a spec sheet (and the parts inside a PC) matters
- Gaming knowledge and hands\-on PC building experience are a must. If you've built your own rig, tuned settings for frame rate, or have strong opinions about GPUs, you'll fit right in here
- A good attitude, a real affinity for technology, and a willingness to chase new areas of expertise as the AI landscape shifts
### Skills:
- Skilled in all Microsoft Office Applications (PowerPoint, Excel, Outlook)
- Familiarity with AI/ML concepts and terminology
- Comfort using AI tools (like ChatGPT or Claude) to support research, drafting marketing copy, and project coordination is a must
- Experience with content management or digital asset workflows a plus
- Experience with any project management or workflow system a plus
### Benefits:
- Full employee benefits package includes 100% Liaison\-paid medical and dental insurance
- Paid vacation/sick and holiday leave
- 401k program with company matching
- Liaison\-paid Short\- and long\-term disability insurance
- Paid group term life insurance
- Optional supplemental insurance and life coverage
- Optional Pet Insurance
- Bright Wellness Program
- Those enrolled in our medical plan get free access to an Employee Assistance Program.
Why Liaison
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We know that you want more than just a job – you want a career with an employer who values you. That’s why we invest so much in our employees with robust benefits, professional development, mentoring, support, stability, and a strong sense of community. It may sound clichéd, but we care about our employees. Seriously.
Salary Context
This $75K-$83K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Liaison Creative + Marketing, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($79K) sits 64% below the category median. Disclosed range: $75K to $83K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Liaison Creative + Marketing AI Hiring
Liaison Creative + Marketing has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $83K - $83K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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